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Posted to commits@systemds.apache.org by ma...@apache.org on 2021/03/17 12:24:49 UTC
[systemds] branch master updated: [SYSTEMDS-2900] New builtin
function sherlock()
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markd pushed a commit to branch master
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new b081a25 [SYSTEMDS-2900] New builtin function sherlock()
b081a25 is described below
commit b081a25e952cea31b788a4a5942390811a9f9fa6
Author: Christian Findenig <ch...@student.tugraz.at>
AuthorDate: Wed Mar 17 00:53:29 2021 +0100
[SYSTEMDS-2900] New builtin function sherlock()
Implementation of a method described in http://sherlock.media.mit.edu/assets/2019-Sherlock-KDD.pdf
DIA Project WS2020/21
Co-authored-by: Christian Findenig <ch...@student.tugraz.at>
Co-authored-by: Jasmin Viktoria Dirnböck <ja...@student.tugraz.at>
Closes #1183
---
docs/site/builtins-reference.md | 121 +
scripts/builtin/sherlock.dml | 185 +
scripts/builtin/sherlockNet.dml | 226 +
scripts/builtin/sherlockPredict.dml | 96 +
.../java/org/apache/sysds/common/Builtins.java | 2 +
.../apache/sysds/runtime/util/UtilFunctions.java | 82 +-
.../systemds/examples/tutorials/sherlockData.py | 108 +
.../test/functions/binary/frame/FrameMapTest.java | 22 +-
.../builtin/BuiltinSherlockPredictTest.java | 100 +
.../functions/builtin/BuiltinSherlockTest.java | 129 +
.../functions/builtin/data/sherlockWeights/cW1.ijv | 288000 ++++++++++++++++++
.../builtin/data/sherlockWeights/cW1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/cW2.ijv | 90000 ++++++
.../builtin/data/sherlockWeights/cW2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/cW3.ijv | 23400 ++
.../builtin/data/sherlockWeights/cW3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/cb1.ijv | 300 +
.../builtin/data/sherlockWeights/cb1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/cb2.ijv | 300 +
.../builtin/data/sherlockWeights/cb2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/cb3.ijv | 78 +
.../builtin/data/sherlockWeights/cb3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/fW1.ijv | 156000 ++++++++++
.../builtin/data/sherlockWeights/fW1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/fW2.ijv | 250000 +++++++++++++++
.../builtin/data/sherlockWeights/fW2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/fW3.ijv | 39000 +++
.../builtin/data/sherlockWeights/fW3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/fb1.ijv | 500 +
.../builtin/data/sherlockWeights/fb1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/fb2.ijv | 500 +
.../builtin/data/sherlockWeights/fb2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/fb3.ijv | 78 +
.../builtin/data/sherlockWeights/fb3.ijv.mtd | 10 +
.../data/sherlockWeights/label_encoding.ijv | 80 +
.../data/sherlockWeights/label_encoding.ijv.mtd | 9 +
.../functions/builtin/data/sherlockWeights/pW1.ijv | 160000 ++++++++++
.../builtin/data/sherlockWeights/pW1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/pW2.ijv | 160000 ++++++++++
.../builtin/data/sherlockWeights/pW2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/pW3.ijv | 31200 ++
.../builtin/data/sherlockWeights/pW3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/pb1.ijv | 400 +
.../builtin/data/sherlockWeights/pb1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/pb2.ijv | 400 +
.../builtin/data/sherlockWeights/pb2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/pb3.ijv | 78 +
.../builtin/data/sherlockWeights/pb3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/sW1.ijv | 2160 +
.../builtin/data/sherlockWeights/sW1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/sW2.ijv | 6400 +
.../builtin/data/sherlockWeights/sW2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/sW3.ijv | 6240 +
.../builtin/data/sherlockWeights/sW3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/sb1.ijv | 72 +
.../builtin/data/sherlockWeights/sb1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/sb2.ijv | 80 +
.../builtin/data/sherlockWeights/sb2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/sb3.ijv | 78 +
.../builtin/data/sherlockWeights/sb3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/wW1.ijv | 40200 +++
.../builtin/data/sherlockWeights/wW1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/wW2.ijv | 40000 +++
.../builtin/data/sherlockWeights/wW2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/wW3.ijv | 15600 +
.../builtin/data/sherlockWeights/wW3.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/wb1.ijv | 200 +
.../builtin/data/sherlockWeights/wb1.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/wb2.ijv | 200 +
.../builtin/data/sherlockWeights/wb2.ijv.mtd | 10 +
.../functions/builtin/data/sherlockWeights/wb3.ijv | 78 +
.../builtin/data/sherlockWeights/wb3.ijv.mtd | 10 +
src/test/scripts/functions/builtin/sherlock.dml | 70 +
.../scripts/functions/builtin/sherlockPredict.dml | 72 +
74 files changed, 1313137 insertions(+), 7 deletions(-)
diff --git a/docs/site/builtins-reference.md b/docs/site/builtins-reference.md
index c4ddffa..d8f8841 100644
--- a/docs/site/builtins-reference.md
+++ b/docs/site/builtins-reference.md
@@ -50,6 +50,8 @@ limitations under the License.
* [`multiLogReg`-Function](#multiLogReg-function)
* [`pnmf`-Function](#pnmf-function)
* [`scale`-Function](#scale-function)
+ * [`sherlock`-Function](#sherlock-function)
+ * [`sherlockPredict`-Function](#sherlockPredict-function)
* [`sigmoid`-Function](#sigmoid-function)
* [`smote`-Function](#smote-function)
* [`steplm`-Function](#steplm-function)
@@ -992,6 +994,125 @@ scale=TRUE;
Y= scale(X,center,scale)
```
+## `sherlock`-Function
+
+Implements training phase of Sherlock: A Deep Learning Approach to Semantic Data Type Detection
+
+[Hulsebos, Madelon, et al. "Sherlock: A deep learning approach to semantic data type detection."
+Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining., 2019]
+### Usage
+
+```r
+sherlock(X_train, y_train)
+```
+
+### Arguments
+
+| Name | Type | Default | Description |
+| :------ | :------------- | -------- | :---------- |
+| X_train | Matrix[Double] | required | Matrix of feature vectors. |
+| y_train | Matrix[Double] | required | Matrix Y of class labels of semantic data type. |
+
+### Returns
+
+| Type | Description |
+| :------------- | :---------- |
+| Matrix[Double] | weights (parameters) matrices for character distribtions |
+| Matrix[Double] | weights (parameters) matrices for word embeddings |
+| Matrix[Double] | weights (parameters) matrices for paragraph vectors |
+| Matrix[Double] | weights (parameters) matrices for global statistics |
+| Matrix[Double] | weights (parameters) matrices for combining all featurs (final)|
+
+### Example
+
+```r
+# preprocessed training data taken from sherlock corpus
+processed_train_values = read("processed/X_train.csv")
+processed_train_labels = read("processed/y_train.csv")
+transform_spec = read("processed/transform_spec.json")
+[processed_train_labels, label_encoding] = sherlock::transform_encode_labels(processed_train_labels, transform_spec)
+write(label_encoding, "weights/label_encoding")
+
+[cW1, cb1,
+ cW2, cb2,
+ cW3, cb3,
+ wW1, wb1,
+ wW2, wb2,
+ wW3, wb3,
+ pW1, pb1,
+ pW2, pb2,
+ pW3, pb3,
+ sW1, sb1,
+ sW2, sb2,
+ sW3, sb3,
+ fW1, fb1,
+ fW2, fb2,
+ fW3, fb3] = sherlock(processed_train_values, processed_train_labels)
+```
+
+## `sherlockPredict`-Function
+
+Implements prediction and evaluation phase of Sherlock: A Deep Learning Approach to Semantic Data Type Detection
+
+[Hulsebos, Madelon, et al. "Sherlock: A deep learning approach to semantic data type detection."
+Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining., 2019]
+### Usage
+
+```r
+sherlockPredict(X, cW1, cb1, cW2, cb2, cW3, cb3, wW1, wb1, wW2, wb2, wW3, wb3,
+ pW1, pb1, pW2, pb2, pW3, pb3, sW1, sb1, sW2, sb2, sW3, sb3,
+ fW1, fb1, fW2, fb2, fW3, fb3)
+```
+### Arguments
+
+| Name | Type | Default | Description |
+| :------ | :------------- | -------- | :---------- |
+| X | Matrix[Double] | required | Matrix of values which are to be classified. |
+| cW | Matrix[Double] | required | Weights (parameters) matrices for character distribtions. |
+| cb | Matrix[Double] | required | Biases vectors for character distribtions. |
+| wW | Matrix[Double] | required | Weights (parameters) matrices for word embeddings. |
+| wb | Matrix[Double] | required | Biases vectors for word embeddings. |
+| pW | Matrix[Double] | required | Weights (parameters) matrices for paragraph vectors. |
+| pb | Matrix[Double] | required | Biases vectors for paragraph vectors. |
+| sW | Matrix[Double] | required | Weights (parameters) matrices for global statistics. |
+| sb | Matrix[Double] | required | Biases vectors for global statistics. |
+| fW | Matrix[Double] | required | Weights (parameters) matrices combining all features (final). |
+| fb | Matrix[Double] | required | Biases vectors combining all features (final). |
+
+### Returns
+
+| Type | Description |
+| :------------- | :---------- |
+| Matrix[Double] | Class probabilities of shape (N, K). |
+### Example
+
+```r
+# preprocessed validation data taken from sherlock corpus
+processed_val_values = read("processed/X_val.csv")
+processed_val_labels = read("processed/y_val.csv")
+transform_spec = read("processed/transform_spec.json")
+label_encoding = read("weights/label_encoding")
+processed_val_labels = sherlock::transform_apply_labels(processed_val_labels, label_encoding, transform_spec)
+
+probs = sherlockPredict(processed_val_values, cW1, cb1,
+cW2, cb2,
+cW3, cb3,
+wW1, wb1,
+wW2, wb2,
+wW3, wb3,
+pW1, pb1,
+pW2, pb2,
+pW3, pb3,
+sW1, sb1,
+sW2, sb2,
+sW3, sb3,
+fW1, fb1,
+fW2, fb2,
+fW3, fb3)
+
+[loss, accuracy] = sherlockPredict::eval(probs, processed_val_labels)
+```
+
## `sigmoid`-Function
The Sigmoid function is a type of activation function, and also defined as a squashing function which limit the output
diff --git a/scripts/builtin/sherlock.dml b/scripts/builtin/sherlock.dml
new file mode 100644
index 0000000..53af20a
--- /dev/null
+++ b/scripts/builtin/sherlock.dml
@@ -0,0 +1,185 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+source("scripts/builtin/sherlockNet.dml") as sherlockNet
+
+# Implements training phase of Sherlock: A Deep Learning Approach to Semantic Data Type Detection
+#
+# [Hulsebos, Madelon, et al. "Sherlock: A deep learning approach to semantic data type detection."
+# Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
+# 2019.]
+
+# Split feature matrix into four different feature categories and train neural networks on the
+# respective single features. Then combine all trained features to train final neural network.
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# X_train Matrix --- maxtrix of feature vectors
+# y_train Matrix --- matrix Y of class labels of semantic data type
+# ---------------------------------------------------------------------------------------------
+# cW Matrix --- weights (parameters) matrices for character distribtions
+# cb Matrix --- biases vectors for character distribtions
+# wW Matrix --- weights (parameters) matrices for word embeddings
+# wb Matrix --- biases vectors for word embeddings
+# pW Matrix --- weights (parameters) matrices for paragraph vectors
+# pb Matrix --- biases vectors for paragraph vectors
+# sW Matrix --- weights (parameters) matrices for global statistics
+# sb Matrix --- biases vectors for global statistics
+# fW Matrix --- weights (parameters) matrices for combining all trained features (final)
+# fb Matrix --- biases vectors for combining all trained features (final)
+# ---------------------------------------------------------------------------------------------
+
+m_sherlock = function(matrix[double] X_train, matrix[double] y_train)
+ return (matrix[double] cW1, matrix[double] cb1,
+ matrix[double] cW2, matrix[double] cb2,
+ matrix[double] cW3, matrix[double] cb3,
+ matrix[double] wW1, matrix[double] wb1,
+ matrix[double] wW2, matrix[double] wb2,
+ matrix[double] wW3, matrix[double] wb3,
+ matrix[double] pW1, matrix[double] pb1,
+ matrix[double] pW2, matrix[double] pb2,
+ matrix[double] pW3, matrix[double] pb3,
+ matrix[double] sW1, matrix[double] sb1,
+ matrix[double] sW2, matrix[double] sb2,
+ matrix[double] sW3, matrix[double] sb3,
+ matrix[double] fW1, matrix[double] fb1,
+ matrix[double] fW2, matrix[double] fb2,
+ matrix[double] fW3, matrix[double] fb3) {
+ train_cols = ncol(X_train)
+ train_rows = nrow(X_train)
+ [cW1, cb1, cW2, cb2, cW3, cb3] = sherlockNet::train(X_train[1:train_rows, 224:1183], y_train, 300)
+ [wW1, wb1, wW2, wb2, wW3, wb3] = sherlockNet::train(cbind(X_train[1:train_rows, 13:212], X_train[1:train_rows, 1188]), y_train, 200)
+ [pW1, pb1, pW2, pb2, pW3, pb3] = sherlockNet::train(X_train[1:train_rows, 1189:1588], y_train, 400)
+ [sW1, sb1, sW2, sb2, sW3, sb3] = sherlockNet::train(cbind(X_train[1:train_rows, 1:12], X_train[1:train_rows, 213:223], X_train[1:train_rows, 1184:1187]), y_train, 80)
+
+ # train features seperate
+ cprobs = sherlockNet::predict(X_train[1:train_rows, 224:1183], cW1, cb1, cW2, cb2, cW3, cb3)
+ wprobs = sherlockNet::predict(cbind(X_train[1:train_rows, 13:212], X_train[1:train_rows, 1188]), wW1,wb1, wW2, wb2, wW3, wb3)
+ pprobs = sherlockNet::predict(X_train[1:train_rows, 1189:1588], pW1, pb1, pW2, pb2, pW3, pb3)
+ sprobs = sherlockNet::predict(cbind(X_train[1:train_rows, 1:12], X_train[1:train_rows, 213:223], X_train[1:train_rows, 1184:1187]), sW1, sb1, sW2, sb2, sW3, sb3)
+
+ #use trained features to train final net
+ first_predictions = cbind(cprobs, wprobs, pprobs, sprobs)
+ [fW1, fb1, fW2, fb2, fW3, fb3] = sherlockNet::train(first_predictions, y_train, 500)
+}
+
+# ---------------------------------------------------------------------------------------------
+# methods to load and transform the data in order to pass valid input to sherlock function
+# ---------------------------------------------------------------------------------------------
+# Function to transform processed X values from the original sherlock project to
+# valid input for sherlock X input matrices.
+
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# data String --- String with processed X values from original sherlock project
+# ---------------------------------------------------------------------------------------------
+# m_data Matrix --- transformed matrix containing X values
+
+transform_values = function(frame[string] data) return (matrix[double] m_data) {
+
+ rows = nrow(data)
+ cols = ncol(data)
+ data = data[1:rows,2:cols]
+
+ #replace True/Fasle with 1/0
+ data = map(data, "d -> d.replace(\"True\",\"1\")")
+ data = map(data, "d -> d.replace(\"False\",\"0\")")
+ m_data = as.matrix(data)
+}
+
+# ---------------------------------------------------------------------------------------------
+# Function to encode the string labels from the original sherlock project (y data)
+# to a numerical representation.
+# ---------------------------------------------------------------------------------------------
+
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# data String --- String labels from original sherlock project
+# transform_spec String --- Transformation specification to encode label column to numerical represenation
+# example: "{ "recode":[ "label" ] }"
+# ---------------------------------------------------------------------------------------------
+# ground_truth Matrix --- matrix containing ground truth in numerical representation
+# meta_data String --- String contaning meta data of transformation encoding
+
+transform_encode_labels = function(frame[string] data, string transform_spec) return (matrix[double] ground_truth , frame[string] meta_data) {
+
+ rows = nrow(data)
+ cols = ncol(data)
+
+ #replace label with number
+ [m_data, meta_data] = transformencode(target=data[1:rows,2:cols],
+ spec=transform_spec)
+
+ ground_truth = matrix(0, rows=rows, cols=78)
+ for ( i in 1:rows) {
+ ground_truth[i, as.scalar(m_data[i,1])] = 1
+ }
+}
+
+# ---------------------------------------------------------------------------------------------
+# Use the encoding from transform_encode_labels() to transform y labels to a
+# numerical representation.
+# ---------------------------------------------------------------------------------------------
+
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# data String --- String labels from original sherlock project
+# meta_data String --- String contaning meta data of transformation encoding
+# transform_spec String --- Transformation specification to encode label column to numerical represenation
+# example: "{ "recode":[ "label" ] }"
+# ---------------------------------------------------------------------------------------------
+# ground_truth Matrix --- matrix containing ground truth in numerical representation
+
+transform_apply_labels = function(frame[string] data, frame[string] meta_data, string transform_spec) return (matrix[double] ground_truth) {
+ #remove index row
+ rows = nrow(data)
+ cols = ncol(data)
+
+ #replace label with number
+ m_data = transformapply(target=data[1:rows,2:cols],
+ spec=transform_spec,
+ meta=meta_data)
+
+ ground_truth = matrix(0, rows=rows, cols=78)
+ for ( i in 1:rows) {
+ ground_truth[i, as.scalar(m_data[i,1])] = 1
+ }
+}
+
+# ---------------------------------------------------------------------------------------------
+# Transform y labels to ground truth
+# ---------------------------------------------------------------------------------------------
+
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# data Matrix --- matrix with labels from original sherlock project
+# ---------------------------------------------------------------------------------------------
+# ground_truth Matrix --- matrix containing ground truth in numerical representation
+
+transform_labels_to_ground_truth = function(matrix[double] data)
+ return(matrix[double] ground_truth) {
+
+ rows = nrow(data)
+ ground_truth = matrix(0, rows=rows, cols=78)
+ for ( i in 1:rows) {
+ ground_truth[i, as.scalar(data[i,1])] = 1
+ }
+}
diff --git a/scripts/builtin/sherlockNet.dml b/scripts/builtin/sherlockNet.dml
new file mode 100644
index 0000000..b29b8dc
--- /dev/null
+++ b/scripts/builtin/sherlockNet.dml
@@ -0,0 +1,226 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+source("nn/layers/affine.dml") as affine
+source("nn/layers/cross_entropy_loss.dml") as cross_entropy_loss
+source("nn/layers/dropout.dml") as dropout
+source("nn/layers/relu.dml") as relu
+source("nn/layers/softmax.dml")as softmax
+source("nn/optim/adam.dml") as adam
+source("scripts/staging/entity-resolution/primitives/evaluation.dml") as evaluation
+
+# Implements Neural Network for Sherlock: A Deep Learning Approach to Semantic Data Type Detection
+#
+# [Hulsebos, Madelon, et al. "Sherlock: A deep learning approach to semantic data type detection."
+# Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
+# 2019.]
+
+# Trains a 2 hidden layer softmax classifier.
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# X_train Matrix --- input data matrix, of shape (N, D)
+# y_train Matrix --- target matrix, of shape (N, K)
+# hidden_layer_neurons int number of neurons per hidden layer
+# ---------------------------------------------------------------------------------------------
+# W Matrix weights (parameters) matrix, of shape (D, M, 3).
+# b Matrix biases vector, of shape (1, M, 3).
+
+train = function(matrix[double] X_train, matrix[double] y_train, int hidden_layer_neurons)
+ return (matrix[double] W1, matrix[double] b1, matrix[double] W2, matrix[double] b2, matrix[double] W3, matrix[double] b3) {
+
+ # Generate input data
+ N = nrow(X_train) # num examples
+ D = ncol(X_train)# num features
+ t = 78 # num target cols
+ print("Training with " + N + " rows, " + D + " cols of data")
+ # Create network:
+ # batch -> affine1 -> relu1 -> dropout1 -> affine2 -> relu2 -> affine3 -> softmax
+ H1 = hidden_layer_neurons # number of neurons in 1st hidden layer
+ H2 = hidden_layer_neurons # number of neurons in 2nd hidden layer
+ p = 0.3 # dropout probability
+ [W1, b1] = affine::init(D, H1, -1)
+ [W2, b2] = affine::init(H1, H2, -1)
+ [W3, b3] = affine::init(H2, t, -1)
+
+ # Initialize Adams parameter
+ initial_lr = 0.0001 # learning rate
+ decay = 0.0001 # learning rate decay constant for weight decay
+
+ beta1 = 0.9; # [0, 1)
+ beta2 = 0.999; # [0, 1)
+ epsilon = 0.00000001;
+ adam_t = 0; # timestamp in adam function
+
+ # Adams optimizer
+ [mW1, vW1] = adam::init(W1);[mb1, vb1] = adam::init(b1)
+ [mW2, vW2] = adam::init(W2);[mb2, vb2] = adam::init(b2)
+ [mW3, vW3] = adam::init(W3);[mb3, vb3] = adam::init(b3)
+
+ # Optimize
+ print("Starting optimization")
+ batch_size = 256 #?
+ epochs = 100
+ iters = ceil(N / batch_size)
+ lr = initial_lr
+ print("init lr: " + initial_lr + " decay: " + decay + "iters: " + iters)
+ for (e in 1:epochs) {
+ for(i in 1:iters){
+ # Get next batch
+ beg = ((i-1) * batch_size) %% N + 1
+ end = min(N, beg + batch_size - 1)
+ X_batch = X_train[beg:end,]
+ y_batch = y_train[beg:end,]
+
+ # Compute forward pass
+ ## layer 1:
+ out1 = affine::forward(X_batch, W1, b1)
+ outr1 = relu::forward(out1)
+ [outd1, maskd1] = dropout::forward(outr1, p, -1)
+ ## layer 2:
+ out2 = affine::forward(outd1, W2, b2)
+ outr2 = relu::forward(out2)
+ ## layer 3:
+ out3 = affine::forward(outr2, W3, b3)
+ probs = softmax::forward(out3)
+
+ if (i==1) {
+ # Compute loss
+ loss = cross_entropy_loss::forward(probs, y_batch)
+ accuracy = mean(rowIndexMax(probs) == rowIndexMax(y_batch))
+
+ # Compute validation loss & accuracy
+ probs_val = predict(X_train, W1, b1, W2, b2, W3, b3)
+ loss_val = cross_entropy_loss::forward(probs_val, y_train)
+ accuracy_val = mean(rowIndexMax(probs_val) == rowIndexMax(y_train))
+
+ # Output results
+ print("Epoch: " + e + ", Iter: " + i + ", Train Loss: " + loss + ", Train Accuracy: "
+ + accuracy + ", Val Loss: " + loss_val + ", Val Accuracy: " + accuracy_val)
+ }
+
+ # Compute backward pass
+ ## loss:
+ dprobs = cross_entropy_loss::backward(probs, y_batch)
+ ## layer 3:
+ dout3 = softmax::backward(dprobs, out3)
+ [doutr2, dW3, db3] = affine::backward(dout3, outr2, W3, b3)
+ ## layer 2:
+ dout2 = relu::backward(doutr2, out2)
+ [doutd1, dW2, db2] = affine::backward(dout2, outd1, W2, b2)
+ ## layer 1:
+ doutr1 = dropout::backward(doutd1, outr1, p, maskd1)
+ dout1 = relu::backward(doutr1, out1)
+ [dX_batch, dW1, db1] = affine::backward(dout1, X_batch, W1, b1)
+
+ # Optimize with Adam
+ [W1, mW1, vW1] = adam::update(W1, dW1, lr, beta1, beta2, epsilon, adam_t, mW1, vW1)
+ [b1, mb1, vb1] = adam::update(b1, db1, lr, beta1, beta2, epsilon, adam_t, mb1, vb1)
+ [W2, mW2, vW2] = adam::update(W2, dW2, lr, beta1, beta2, epsilon, adam_t, mW2, vW2)
+ [b2, mb2, vb2] = adam::update(b2, db2, lr, beta1, beta2, epsilon, adam_t, mb2, vb2)
+ [W3, mW3, vW3] = adam::update(W3, dW3, lr, beta1, beta2, epsilon, adam_t, mW3, vW3)
+ [b3, mb3, vb3] = adam::update(b3, db3, lr, beta1, beta2, epsilon, adam_t, mb3, vb3)
+ }
+
+ # Decay learning rate
+ adam_t = adam_t + 1
+ lr = initial_lr * (1 / (1 + decay * e))
+ }
+}
+
+# Computes the class probability predictions of a softmax classifier
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# test_val Matrix --- input data matrix, of shape (N, D) each with D features
+# W Matrix weights (parameters) matrix, of shape (D, M, 3).
+# b Matrix biases vector, of shape (1, M, 3).
+# ---------------------------------------------------------------------------------------------
+# probs Matrix class probabilities of shape (N, K)
+
+predict = function(matrix[double] test_val,
+ matrix[double] W1, matrix[double] b1,
+ matrix[double] W2, matrix[double] b2,
+ matrix[double] W3, matrix[double] b3)
+ return (matrix[double] probs) {
+
+ N = nrow(test_val)
+ K = ncol(W3) # num features
+
+ # Network:
+ # batch -> affine1 -> relu1 -> affine2 -> relu2 -> affine3 -> softmax
+ # Compute predictions over mini-batches
+
+ probs = matrix(0, rows=N, cols=K)
+
+ batch_size = 128
+ iters = ceil(N / batch_size)
+ for(i in 1:iters) {
+ # Get next batch
+ beg = ((i-1) * batch_size) %% N + 1
+ end = min(N, beg + batch_size - 1)
+ end = min(end, N)
+ X_batch = test_val[beg:end,]
+
+ # Compute forward pass
+ ## layer 1:
+ out1 = affine::forward(X_batch, W1, b1)
+ outr1 = relu::forward(out1)
+ ## layer 2:
+ out2 = affine::forward(outr1, W2, b2)
+ outr2 = relu::forward(out2)
+ ## layer 3:
+ out3 = affine::forward(outr2, W3, b3)
+ probs_batch = softmax::forward(out3)
+
+ # Store predictions
+ probs[beg:end,] = probs_batch
+ }
+}
+
+# Evaluates the performance of the network.
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# probs Matrix class probabilities of shape (N, K) (one-hot encoded)
+# Y Matrix target matrix of shape (N, K)
+# ---------------------------------------------------------------------------------------------
+# loss double scalar loss, of shape (1)
+# accuracy double scalar accuracy, of shape (1)
+# f1 score double scalar f1 score, of shape (1)
+# precision double scalar precission, of shape (1)
+# recall double scalar recall, of shape (1)
+
+eval = function(matrix[double] probs, matrix[double] Y)
+ return (double loss, double accuracy, double f1, double precision, double recall) {
+ # Compute loss & accuracy
+ loss = cross_entropy_loss::forward(probs, Y)
+ correct_pred = rowIndexMax(probs) == rowIndexMax(Y)
+ accuracy = mean(correct_pred)
+
+ #calc f1 score
+ rows = nrow(Y)
+ cols = ncol(Y)
+ predBooleanMatrix = matrix(0, rows=rows, cols=cols)
+ for ( i in 1:rows) {
+ predBooleanMatrix[i, as.scalar(rowIndexMax(probs[i,1:cols]))] = 1
+ }
+ [f1, precision, recall] = evaluation::f1(predBooleanMatrix, Y)
+}
diff --git a/scripts/builtin/sherlockPredict.dml b/scripts/builtin/sherlockPredict.dml
new file mode 100644
index 0000000..9db9251
--- /dev/null
+++ b/scripts/builtin/sherlockPredict.dml
@@ -0,0 +1,96 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+source("scripts/builtin/sherlockNet.dml") as sherlockNet
+
+# Implements prediction and evaluation phase of Sherlock:
+# A Deep Learning Approach to Semantic Data Type Detection
+#
+# [Hulsebos, Madelon, et al. "Sherlock: A deep learning approach to semantic data type detection."
+# Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
+# 2019.]
+
+# Split feature matrix into four different feature categories and predicting the class probability
+# on the respective features. Then combine all predictions for final predicted probabilities.
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# X Matrix --- matrix of values which are to be classified
+# cW Matrix --- weights (parameters) matrices for character distribtions
+# cb Matrix --- biases vectors for character distribtions
+# wW Matrix --- weights (parameters) matrices for word embeddings
+# wb Matrix --- biases vectors for word embeddings
+# pW Matrix --- weights (parameters) matrices for paragraph vectors
+# pb Matrix --- biases vectors for paragraph vectors
+# sW Matrix --- weights (parameters) matrices for global statistics
+# sb Matrix --- biases vectors for global statistics
+# fW Matrix --- weights (parameters) matrices for combining all trained features (final)
+# fb Matrix --- biases vectors for combining all trained features (final)
+# ---------------------------------------------------------------------------------------------
+# probs Matrix class probabilities of shape (N, K)
+
+m_sherlockPredict = function(matrix[double] X,
+ matrix[double] cW1, matrix[double] cb1,
+ matrix[double] cW2, matrix[double] cb2,
+ matrix[double] cW3, matrix[double] cb3,
+ matrix[double] wW1, matrix[double] wb1,
+ matrix[double] wW2, matrix[double] wb2,
+ matrix[double] wW3, matrix[double] wb3,
+ matrix[double] pW1, matrix[double] pb1,
+ matrix[double] pW2, matrix[double] pb2,
+ matrix[double] pW3, matrix[double] pb3,
+ matrix[double] sW1, matrix[double] sb1,
+ matrix[double] sW2, matrix[double] sb2,
+ matrix[double] sW3, matrix[double] sb3,
+ matrix[double] fW1, matrix[double] fb1,
+ matrix[double] fW2, matrix[double] fb2,
+ matrix[double] fW3, matrix[double] fb3)
+ return (matrix[double] probs) {
+
+ rows = nrow(X)
+
+ cprobs = sherlockNet::predict(X[1:rows, 224:1183], cW1, cb1, cW2, cb2, cW3, cb3)
+ wprobs = sherlockNet::predict(cbind(X[1:rows, 13:212], X[1:rows, 1188]), wW1,wb1, wW2, wb2, wW3, wb3)
+ pprobs = sherlockNet::predict(X[1:rows, 1189:1588], pW1, pb1, pW2, pb2, pW3, pb3)
+ sprobs = sherlockNet::predict(cbind(X[1:rows, 1:12], X[1:rows, 213:223], X[1:rows, 1184:1187]), sW1, sb1, sW2, sb2, sW3, sb3)
+
+ first_predictions = cbind(cprobs, wprobs, pprobs, sprobs)
+ #final training
+ probs = sherlockNet::predict(first_predictions, fW1, fb1, fW2, fb2, fW3, fb3)
+}
+
+# Evaluates the performance of the network.
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# probs Matrix class probabilities of shape (N, K) (one-hot encoded)
+# Y Matrix target matrix of shape (N, K)
+# ---------------------------------------------------------------------------------------------
+# loss double scalar loss, of shape (1)
+# accuracy double scalar accuracy, of shape (1)
+# f1 score double scalar f1 score, of shape (1)
+# precision double scalar precission, of shape (1)
+# recall double scalar recall, of shape (1)
+
+eval = function(matrix[double] probs, matrix[double] Y)
+ return (double loss, double accuracy, double f1, double precision, double recall) {
+
+ [loss, accuracy, f1, precision, recall] = sherlockNet::eval(probs, Y)
+}
diff --git a/src/main/java/org/apache/sysds/common/Builtins.java b/src/main/java/org/apache/sysds/common/Builtins.java
index 377d6e5..8854f69 100644
--- a/src/main/java/org/apache/sysds/common/Builtins.java
+++ b/src/main/java/org/apache/sysds/common/Builtins.java
@@ -210,6 +210,8 @@ public enum Builtins {
SAMPLE("sample", false),
SD("sd", false),
SEQ("seq", false),
+ SHERLOCK("sherlock", true),
+ SHERLOCKPREDICT("sherlockPredict", true),
SIGMOID("sigmoid", true), // 1 / (1 + exp(-X))
SIGN("sign", false),
SIN("sin", false),
diff --git a/src/main/java/org/apache/sysds/runtime/util/UtilFunctions.java b/src/main/java/org/apache/sysds/runtime/util/UtilFunctions.java
index fa0fc1e..b9a43d4 100644
--- a/src/main/java/org/apache/sysds/runtime/util/UtilFunctions.java
+++ b/src/main/java/org/apache/sysds/runtime/util/UtilFunctions.java
@@ -33,7 +33,18 @@ import org.apache.sysds.runtime.meta.TensorCharacteristics;
import java.text.ParseException;
import java.text.SimpleDateFormat;
-import java.util.*;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.BitSet;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.LinkedHashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+import java.util.Set;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
public class UtilFunctions {
// private static final Log LOG = LogFactory.getLog(UtilFunctions.class.getName());
@@ -852,17 +863,76 @@ public class UtilFunctions {
.map(DATE_FORMATS::get).orElseThrow(() -> new NullPointerException("Unknown date format."));
}
- public static double jaccardSim(String x, String y) {
+ public static FrameBlock getSplittedString (String input) {
+ //Frame f = new Frame();
+ String[] string_array = input.split("'[ ]*,[ ]*'");
+ ValueType[] schema = new ValueType[string_array.length];
+ for(int i=0; i< string_array.length; i++)
+ schema[i] = ValueType.STRING;
+ FrameBlock fb = new FrameBlock(schema);
+ fb.appendRow(string_array);
+ List<String>r = Arrays.asList(string_array);
+ System.out.println("converted FrameBlock: " + fb.toString());
+ return fb;//.subList(0,2);
+ }
+
+ public static String[] getSplittedStringAsArray (String input) {
+ //Frame f = new Frame();
+ String[] string_array = input.split("'[ ]*,[ ]*'");
+ return string_array;//.subList(0,2);
+ }
+
+ public static double jaccardSim(String x, String y) {
Set<String> charsX = new LinkedHashSet<>(Arrays.asList(x.split("(?!^)")));
Set<String> charsY = new LinkedHashSet<>(Arrays.asList(y.split("(?!^)")));
-
+
final int sa = charsX.size();
final int sb = charsY.size();
charsX.retainAll(charsY);
final int intersection = charsX.size();
return 1d / (sa + sb - charsX.size()) * intersection;
}
-
+
+ public static String columnStringToCSVString(String input, String separator) {
+ StringBuffer sb = new StringBuffer(input);
+ StringBuilder outStringBuilder = new StringBuilder();
+ String[] string_array;
+
+ // remove leading and trailing brackets: []
+ int startOfArray = sb.indexOf("\"[");
+ if (startOfArray >=0)
+ sb.delete(startOfArray, startOfArray + 2);
+
+
+ int endOfArray = sb.lastIndexOf("]\"");
+ if (endOfArray >=0)
+ sb.delete(endOfArray, endOfArray + 2);
+
+
+ // split values depending on their format
+ if (sb.indexOf("'") != -1) { // string contains strings
+ // replace "None" with "'None'"
+ Pattern p = Pattern.compile(", None,");
+ Matcher m = p.matcher(sb);
+ string_array = m.replaceAll(", 'None',").split("'[ ]*,[ ]*'");
+
+ // remove apostrophe in first and last string element
+ string_array[0] = string_array[0].replaceFirst("'", "");
+ int lastArrayIndex = string_array.length - 1;
+ string_array[lastArrayIndex] = string_array[lastArrayIndex]
+ .substring(0, string_array[lastArrayIndex].length() - 1);
+ }
+ else // string contains numbers only
+ string_array = sb.toString().split(",");
+
+ // select a suitable separator that can be used to read in the file properly
+ for(String s : string_array)
+ outStringBuilder.append(s).append(separator);
+
+ outStringBuilder.delete(outStringBuilder.length() - separator.length(), outStringBuilder.length());
+ return outStringBuilder.toString();
+ }
+
/**
* Generates a random FrameBlock with given parameters.
*
@@ -872,7 +942,7 @@ public class UtilFunctions {
* @param random random number generator
* @return FrameBlock
*/
- public static FrameBlock generateRandomFrameBlock(int rows, int cols, ValueType[] schema, Random random){
+ public static FrameBlock generateRandomFrameBlock(int rows, int cols, ValueType[] schema, Random random) {
String[] names = new String[cols];
for(int i = 0; i < cols; i++)
names[i] = schema[i].toString();
@@ -891,7 +961,7 @@ public class UtilFunctions {
* @param random random number generator
* @return Object
*/
- public static Object generateRandomValueFromValueType(ValueType valueType, Random random){
+ public static Object generateRandomValueFromValueType(ValueType valueType, Random random) {
switch (valueType){
case FP32: return random.nextFloat();
case FP64: return random.nextDouble();
diff --git a/src/main/python/systemds/examples/tutorials/sherlockData.py b/src/main/python/systemds/examples/tutorials/sherlockData.py
new file mode 100644
index 0000000..760b455
--- /dev/null
+++ b/src/main/python/systemds/examples/tutorials/sherlockData.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+# -------------------------------------------------------------
+#
+# 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 os
+from google_drive_downloader import GoogleDriveDownloader as gd
+import pandas as pd
+
+class SherlockData:
+ '''
+ This data set holds data for semantic data type detection.
+
+ The data can be used to train and test the sherlock network implemented in sherlock.dml
+ The X_*.csv files have to be processed with the function: sherlock::transform_values()
+ The y_*.csv files have to be processed with the function: \n
+ sherlock::transform_encode_labels() to encode the output categories to numbers.\n
+ sherlock::transform_apply_labels() is used to apply the above created encoding to the remaining input files.
+ The processed files can then be used to train the network: \n
+ sherlock(X_train, y_train)
+ '''
+
+ _base_path: str
+ _processed_dir: str
+ _raw_dir: str
+
+ def __init__(self):
+ self._base_path = "systemds/examples/tutorials/sherlock/"
+ self._processed_dir = "data/processed/"
+ self._raw_dir = "data/raw/"
+
+ def parquet_to_csv(self, parquetfile):
+ print(f'got file: {parquetfile}')
+ df = pd.read_parquet(parquetfile)
+ dest_name = str(parquetfile).replace("parquet", "csv")
+ df.to_csv(dest_name)
+
+ def get_train_values(self, processed):
+ return self._get_values(processed=processed, type="train")
+
+ def get_val_values(self, processed):
+ return self._get_values(processed=processed, type="val")
+
+ def get_test_values(self, processed):
+ return self._get_values(processed=processed, type="test")
+
+ def get_train_labels(self, processed):
+ return self._get_labels(processed=processed, type="train")
+
+ def get_val_labels(self, processed):
+ return self._get_labels(processed=processed, type="val")
+
+ def get_test_labels(self, processed):
+ return self._get_labels(processed=processed, type="test")
+
+ def _get_values(self, processed, type):
+ filename_parquet = self._base_path
+ filename_parquet += self._processed_dir + "X_{}.parquet".format(type) \
+ if processed ==True else self._raw_dir + "/{}_values.parquet".format(type)
+
+ if not os.path.exists(filename_parquet):
+ self._download_data(self._base_path)
+ return pd.read_parquet(filename_parquet)
+
+ def _get_labels(self, processed, type):
+ filename_parquet = self._base_path
+ filename_parquet += self._processed_dir + "y_{}.parquet".format(type) \
+ if processed ==True else self._raw_dir + "{}_labels.parquet".format(type)
+
+ if not os.path.exists(filename_parquet):
+ self._download_data(self._base_path)
+ return pd.read_parquet(filename_parquet)
+
+ def _download_data(self, data_dir):
+ """Download raw and preprocessed data files.
+ The data is downloaded from Google Drive and stored in the 'data/' directory.
+ """
+ print(f"Downloading the raw and preprocessed data into {data_dir}.")
+
+ if not os.path.exists(data_dir + "data"):
+ os.makedirs(data_dir, exist_ok=True)
+ print('Downloading data directory.')
+ filename = data_dir + "data.zip"
+ gd.download_file_from_google_drive(
+ file_id='1-g0zbKFAXz7zKZc0Dnh74uDBpZCv4YqU',
+ dest_path=filename,
+ unzip=True,
+ showsize=True
+ )
+
+ print('Data was downloaded.')
diff --git a/src/test/java/org/apache/sysds/test/functions/binary/frame/FrameMapTest.java b/src/test/java/org/apache/sysds/test/functions/binary/frame/FrameMapTest.java
index 602be07..ca25988 100644
--- a/src/test/java/org/apache/sysds/test/functions/binary/frame/FrameMapTest.java
+++ b/src/test/java/org/apache/sysds/test/functions/binary/frame/FrameMapTest.java
@@ -22,6 +22,7 @@ package org.apache.sysds.test.functions.binary.frame;
import org.apache.sysds.common.Types;
import org.apache.sysds.common.Types.FileFormat;
import org.apache.sysds.lops.LopProperties.ExecType;
+import org.apache.sysds.runtime.io.FileFormatPropertiesCSV;
import org.apache.sysds.runtime.io.FrameWriterFactory;
import org.apache.sysds.runtime.matrix.data.FrameBlock;
import org.apache.sysds.runtime.util.UtilFunctions;
@@ -46,7 +47,8 @@ public class FrameMapTest extends AutomatedTestBase {
CHAR_AT,
REPLACE,
UPPER_CASE,
- DATE_UTILS
+ DATE_UTILS,
+ SHERLOCK_PREP
}
@BeforeClass
public static void init() {
@@ -109,6 +111,12 @@ public class FrameMapTest extends AutomatedTestBase {
runDmlMapTest("x -> UtilFunctions.toMillis(x)", TestType.DATE_UTILS, ExecType.SPARK);
}
+ @Test
+ public void testColumnStringToSherlockFeatures() {
+ runDmlMapTest("x -> UtilFunctions.columnStringToCSVString(x,\"§§\")", TestType.SHERLOCK_PREP, ExecType.SPARK);
+ }
+
+
private void runDmlMapTest( String expression, TestType type, ExecType et)
{
Types.ExecMode platformOld = setExecMode(et);
@@ -128,6 +136,14 @@ public class FrameMapTest extends AutomatedTestBase {
FrameWriterFactory.createFrameWriter(FileFormat.CSV).
writeFrameToHDFS(new FrameBlock(schemaStrings1, date), input("A"), rows2, 1);
}
+ else if(type == TestType.SHERLOCK_PREP) {
+ String[][] data = new String[1][1];
+ data[0][0] = "\"['Global', 'United States', 'Australia']\"";
+ FileFormatPropertiesCSV ffp = new FileFormatPropertiesCSV();
+ ffp.setDelim(";");
+ FrameWriterFactory.createFrameWriter(FileFormat.CSV, ffp).
+ writeFrameToHDFS(new FrameBlock(schemaStrings1, data), input("A"), 1, 1);
+ }
else {
double[][] A = getRandomMatrix(rows, 1, 0, 1, 1, 2);
writeInputFrameWithMTD("A", A, true, schemaStrings1, FileFormat.CSV);
@@ -164,6 +180,10 @@ public class FrameMapTest extends AutomatedTestBase {
for(int i =0; i<input.length; i++)
TestUtils.compareScalars(String.valueOf(UtilFunctions.toMillis(input[i])), output[i]);
break;
+ case SHERLOCK_PREP:
+ for(int i =0; i<input.length; i++)
+ TestUtils.compareScalars(UtilFunctions.columnStringToCSVString(input[i],"§§"), output[i]);
+ break;
}
}
catch (Exception ex) {
diff --git a/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSherlockPredictTest.java b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSherlockPredictTest.java
new file mode 100644
index 0000000..35d2a65
--- /dev/null
+++ b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSherlockPredictTest.java
@@ -0,0 +1,100 @@
+/*
+ * 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.
+ */
+
+package org.apache.sysds.test.functions.builtin;
+
+import org.apache.sysds.runtime.matrix.data.MatrixValue;
+import org.apache.sysds.test.AutomatedTestBase;
+import org.apache.sysds.test.TestConfiguration;
+import org.junit.Assert;
+import org.junit.Test;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+
+public class BuiltinSherlockPredictTest extends AutomatedTestBase {
+ private final static String TEST_NAME = "sherlockPredict";
+ private final static String TEST_DIR = "functions/builtin/";
+ private final static String TEST_CLASS_DIR = TEST_DIR + BuiltinSherlockPredictTest.class.getSimpleName() + "/";
+ private final static String WEIGHTS_DIR = SCRIPT_DIR + TEST_DIR + "data/sherlockWeights/";
+
+ @Override public void setUp() {
+ addTestConfiguration(TEST_NAME, new TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[] {"B"}));
+ }
+
+ @Test public void testSherlockPredict() {
+ runTestSherlockPredict();
+ }
+
+ private void runTestSherlockPredict() {
+ loadTestConfiguration(getTestConfiguration(TEST_NAME));
+ String HOME = SCRIPT_DIR + TEST_DIR;
+ fullDMLScriptName = HOME + TEST_NAME + ".dml";
+
+ List<String> proArgs = new ArrayList<>();
+ proArgs.add("-exec");
+ proArgs.add(" singlenode");
+ proArgs.add("-nvargs");
+ proArgs.add("X=" + input("X"));
+ proArgs.add("cW1=" + WEIGHTS_DIR + "cW1.ijv");
+ proArgs.add("cb1=" + WEIGHTS_DIR + "cb1.ijv");
+ proArgs.add("cW2=" + WEIGHTS_DIR + "cW2.ijv");
+ proArgs.add("cb2=" + WEIGHTS_DIR + "cb2.ijv");
+ proArgs.add("cW3=" + WEIGHTS_DIR + "cW3.ijv");
+ proArgs.add("cb3=" + WEIGHTS_DIR + "cb3.ijv");
+ proArgs.add("wW1=" + WEIGHTS_DIR + "wW1.ijv");
+ proArgs.add("wb1=" + WEIGHTS_DIR + "wb1.ijv");
+ proArgs.add("wW2=" + WEIGHTS_DIR + "wW2.ijv");
+ proArgs.add("wb2=" + WEIGHTS_DIR + "wb2.ijv");
+ proArgs.add("wW3=" + WEIGHTS_DIR + "wW3.ijv");
+ proArgs.add("wb3=" + WEIGHTS_DIR + "wb3.ijv");
+ proArgs.add("pW1=" + WEIGHTS_DIR + "pW1.ijv");
+ proArgs.add("pb1=" + WEIGHTS_DIR + "pb1.ijv");
+ proArgs.add("pW2=" + WEIGHTS_DIR + "pW2.ijv");
+ proArgs.add("pb2=" + WEIGHTS_DIR + "pb2.ijv");
+ proArgs.add("pW3=" + WEIGHTS_DIR + "pW3.ijv");
+ proArgs.add("pb3=" + WEIGHTS_DIR + "pb3.ijv");
+ proArgs.add("sW1=" + WEIGHTS_DIR + "sW1.ijv");
+ proArgs.add("sb1=" + WEIGHTS_DIR + "sb1.ijv");
+ proArgs.add("sW2=" + WEIGHTS_DIR + "sW2.ijv");
+ proArgs.add("sb2=" + WEIGHTS_DIR + "sb2.ijv");
+ proArgs.add("sW3=" + WEIGHTS_DIR + "sW3.ijv");
+ proArgs.add("sb3=" + WEIGHTS_DIR + "sb3.ijv");
+ proArgs.add("fW1=" + WEIGHTS_DIR + "fW1.ijv");
+ proArgs.add("fb1=" + WEIGHTS_DIR + "fb1.ijv");
+ proArgs.add("fW2=" + WEIGHTS_DIR + "fW2.ijv");
+ proArgs.add("fb2=" + WEIGHTS_DIR + "fb2.ijv");
+ proArgs.add("fW3=" + WEIGHTS_DIR + "fW3.ijv");
+ proArgs.add("fb3=" + WEIGHTS_DIR + "fb3.ijv");
+ proArgs.add("probs=" + output("probs"));
+ programArgs = proArgs.toArray(new String[0]);
+
+ double[][] X = getRandomMatrix(256, 1588, 0, 3, 0.9, 7);
+
+ writeInputMatrixWithMTD("X", X, true);
+ runTest(true, EXCEPTION_NOT_EXPECTED, null, -1);
+
+
+ //read probs
+ HashMap<MatrixValue.CellIndex, Double> probs = readDMLMatrixFromOutputDir("probs");
+ // ToDo: add check to pass/fail this test
+ Assert.assertFalse(probs.isEmpty());
+ }
+}
diff --git a/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSherlockTest.java b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSherlockTest.java
new file mode 100644
index 0000000..e1ba245
--- /dev/null
+++ b/src/test/java/org/apache/sysds/test/functions/builtin/BuiltinSherlockTest.java
@@ -0,0 +1,129 @@
+/*
+ * 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.
+ */
+
+package org.apache.sysds.test.functions.builtin;
+
+import org.apache.sysds.runtime.matrix.data.MatrixValue;
+import org.apache.sysds.test.AutomatedTestBase;
+import org.apache.sysds.test.TestConfiguration;
+import org.apache.sysds.test.TestUtils;
+import org.junit.Test;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+
+public class BuiltinSherlockTest extends AutomatedTestBase {
+ private final static String TEST_NAME = "sherlock";
+ private final static String TEST_DIR = "functions/builtin/";
+ private final static String TEST_CLASS_DIR = TEST_DIR + BuiltinSherlockTest.class.getSimpleName() + "/";
+
+ @Override public void setUp() {
+ TestUtils.clearAssertionInformation();
+ addTestConfiguration(TEST_NAME, new TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[] {"B"}));
+ }
+
+ @Test public void testSherlock() {
+ runtestSherlock();
+ }
+
+ private void runtestSherlock() {
+ loadTestConfiguration(getTestConfiguration(TEST_NAME));
+ String HOME = SCRIPT_DIR + TEST_DIR;
+ fullDMLScriptName = HOME + TEST_NAME + ".dml";
+
+ List<String> proArgs = new ArrayList<>();
+ proArgs.add("-exec");
+ proArgs.add(" singlenode");
+ proArgs.add("-nvargs");
+ proArgs.add("X=" + input("X"));
+ proArgs.add("Y=" + input("Y"));
+
+ proArgs.add("cW1=" + output("cW1"));
+ proArgs.add("cb1=" + output("cb1"));
+ proArgs.add("cW2=" + output("cW2"));
+ proArgs.add("cb2=" + output("cb2"));
+ proArgs.add("cW3=" + output("cW3"));
+ proArgs.add("cb3=" + output("cb3"));
+ proArgs.add("wW1=" + output("wW1"));
+ proArgs.add("wb1=" + output("wb1"));
+ proArgs.add("wW2=" + output("wW2"));
+ proArgs.add("wb2=" + output("wb2"));
+ proArgs.add("wW3=" + output("wW3"));
+ proArgs.add("wb3=" + output("wb3"));
+ proArgs.add("pW1=" + output("pW1"));
+ proArgs.add("pb1=" + output("pb1"));
+ proArgs.add("pW2=" + output("pW2"));
+ proArgs.add("pb2=" + output("pb2"));
+ proArgs.add("pW3=" + output("pW3"));
+ proArgs.add("pb3=" + output("pb3"));
+ proArgs.add("sW1=" + output("sW1"));
+ proArgs.add("sb1=" + output("sb1"));
+ proArgs.add("sW2=" + output("sW2"));
+ proArgs.add("sb2=" + output("sb2"));
+ proArgs.add("sW3=" + output("sW3"));
+ proArgs.add("sb3=" + output("sb3"));
+ proArgs.add("fW1=" + output("fW1"));
+ proArgs.add("fb1=" + output("fb1"));
+ proArgs.add("fW2=" + output("fW2"));
+ proArgs.add("fb2=" + output("fb2"));
+ proArgs.add("fW3=" + output("fW3"));
+ proArgs.add("fb3=" + output("fb3"));
+ programArgs = proArgs.toArray(new String[proArgs.size()]);
+
+ double[][] X = getRandomMatrix(256, 1588, 0, 3, 0.9, 7);
+ double[][] Y = getRandomMatrix(256, 78, 0, 1, 0.9, 7);
+
+ writeInputMatrixWithMTD("X", X, true);
+ writeInputMatrixWithMTD("Y", Y, true);
+ runTest(true, EXCEPTION_NOT_EXPECTED, null, -1);
+
+ //compare expected results
+ HashMap<MatrixValue.CellIndex, Double> cW1 = readDMLMatrixFromOutputDir("cW1");
+ HashMap<MatrixValue.CellIndex, Double> cb1 = readDMLMatrixFromOutputDir("cb1");
+ HashMap<MatrixValue.CellIndex, Double> cW2 = readDMLMatrixFromOutputDir("cW2");
+ HashMap<MatrixValue.CellIndex, Double> cb2 = readDMLMatrixFromOutputDir("cb2");
+ HashMap<MatrixValue.CellIndex, Double> cW3 = readDMLMatrixFromOutputDir("cW3");
+ HashMap<MatrixValue.CellIndex, Double> cb3 = readDMLMatrixFromOutputDir("cb3");
+ HashMap<MatrixValue.CellIndex, Double> wW1 = readDMLMatrixFromOutputDir("wW1");
+ HashMap<MatrixValue.CellIndex, Double> wb1 = readDMLMatrixFromOutputDir("wb1");
+ HashMap<MatrixValue.CellIndex, Double> wW2 = readDMLMatrixFromOutputDir("wW2");
+ HashMap<MatrixValue.CellIndex, Double> wb2 = readDMLMatrixFromOutputDir("wb2");
+ HashMap<MatrixValue.CellIndex, Double> wW3 = readDMLMatrixFromOutputDir("wW3");
+ HashMap<MatrixValue.CellIndex, Double> wb3 = readDMLMatrixFromOutputDir("wb3");
+ HashMap<MatrixValue.CellIndex, Double> pW1 = readDMLMatrixFromOutputDir("pW1");
+ HashMap<MatrixValue.CellIndex, Double> pb1 = readDMLMatrixFromOutputDir("pb1");
+ HashMap<MatrixValue.CellIndex, Double> pW2 = readDMLMatrixFromOutputDir("pW2");
+ HashMap<MatrixValue.CellIndex, Double> pb2 = readDMLMatrixFromOutputDir("pb2");
+ HashMap<MatrixValue.CellIndex, Double> pW3 = readDMLMatrixFromOutputDir("pW3");
+ HashMap<MatrixValue.CellIndex, Double> pb3 = readDMLMatrixFromOutputDir("pb3");
+ HashMap<MatrixValue.CellIndex, Double> sW1 = readDMLMatrixFromOutputDir("sW1");
+ HashMap<MatrixValue.CellIndex, Double> sb1 = readDMLMatrixFromOutputDir("sb1");
+ HashMap<MatrixValue.CellIndex, Double> sW2 = readDMLMatrixFromOutputDir("sW2");
+ HashMap<MatrixValue.CellIndex, Double> sb2 = readDMLMatrixFromOutputDir("sb2");
+ HashMap<MatrixValue.CellIndex, Double> sW3 = readDMLMatrixFromOutputDir("sW3");
+ HashMap<MatrixValue.CellIndex, Double> sb3 = readDMLMatrixFromOutputDir("sb3");
+ HashMap<MatrixValue.CellIndex, Double> fW1 = readDMLMatrixFromOutputDir("fW1");
+ HashMap<MatrixValue.CellIndex, Double> fb1 = readDMLMatrixFromOutputDir("fb1");
+ HashMap<MatrixValue.CellIndex, Double> fW2 = readDMLMatrixFromOutputDir("fW2");
+ HashMap<MatrixValue.CellIndex, Double> fb2 = readDMLMatrixFromOutputDir("fb2");
+ HashMap<MatrixValue.CellIndex, Double> fW3 = readDMLMatrixFromOutputDir("fW3");
+ HashMap<MatrixValue.CellIndex, Double> fb3 = readDMLMatrixFromOutputDir("fb3");
+ }
+}
diff --git a/src/test/scripts/functions/builtin/data/sherlockWeights/cW1.ijv b/src/test/scripts/functions/builtin/data/sherlockWeights/cW1.ijv
new file mode 100644
index 0000000..d73ee23
--- /dev/null
+++ b/src/test/scripts/functions/builtin/data/sherlockWeights/cW1.ijv
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