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nkak pushed a commit to branch master
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The following commit(s) were added to refs/heads/master by this push:
new df03bc2 DL: Add function for predict byom
df03bc2 is described below
commit df03bc2029fffa081685c9ffd9471386666bc9a6
Author: Nikhil Kak <n...@pivotal.io>
AuthorDate: Fri Jul 19 17:39:27 2019 -0700
DL: Add function for predict byom
JIRA: MADLIB-1371 , MADLIB-1359
Previously a user would have to train a deep learning model in madlib
and only then they could use that model to predict.
This commit adds a new function called `madlib_keras_predict_byom` which
allows the
user to run prediction on their own model which doesn't have to be
trained on madlib.
* Refactored the code to reuse the logic between predict and
predict_byom
* user doc changes
Co-authored-by: Nandish Jayaram <njaya...@apache.org>
Co-authored-by: Orhan Kislal <okis...@apache.org>
---
.../deep_learning/input_data_preprocessor.py_in | 2 +-
.../modules/deep_learning/madlib_keras.py_in | 71 +++--
.../modules/deep_learning/madlib_keras.sql_in | 337 +++++++++++++++++++-
.../deep_learning/madlib_keras_helper.py_in | 2 +
.../deep_learning/madlib_keras_predict.py_in | 312 +++++++++++++++----
.../deep_learning/madlib_keras_validator.py_in | 346 ++++++++++-----------
.../model_arch_info.py_in | 36 +++
.../modules/deep_learning/test/madlib_keras.sql_in | 196 +++++++++---
.../test/unit_tests/test_madlib_keras.py_in | 203 +++++++++---
9 files changed, 1140 insertions(+), 365 deletions(-)
diff --git
a/src/ports/postgres/modules/deep_learning/input_data_preprocessor.py_in
b/src/ports/postgres/modules/deep_learning/input_data_preprocessor.py_in
index 82bec97..6a03eca 100644
--- a/src/ports/postgres/modules/deep_learning/input_data_preprocessor.py_in
+++ b/src/ports/postgres/modules/deep_learning/input_data_preprocessor.py_in
@@ -58,7 +58,7 @@ class InputDataPreprocessorDL(object):
self.dependent_varname = dependent_varname
self.independent_varname = independent_varname
self.buffer_size = buffer_size
- self.normalizing_const = normalizing_const if normalizing_const is not
None else 1.0
+ self.normalizing_const = normalizing_const if normalizing_const is not
None else DEFAULT_NORMALIZING_CONST
self.num_classes = num_classes
self.module_name = module_name
self.output_summary_table = None
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras.py_in
b/src/ports/postgres/modules/deep_learning/madlib_keras.py_in
index 47ce306..fa55093 100644
--- a/src/ports/postgres/modules/deep_learning/madlib_keras.py_in
+++ b/src/ports/postgres/modules/deep_learning/madlib_keras.py_in
@@ -29,25 +29,19 @@ import time
import keras
from keras import backend as K
-from keras import utils as keras_utils
from keras.layers import *
from keras.models import *
from keras.optimizers import *
from keras.regularizers import *
-import madlib_keras_serializer
from madlib_keras_helper import *
from madlib_keras_validator import *
from madlib_keras_wrapper import *
-from keras_model_arch_table import ModelArchSchema
+from model_arch_info import *
-from utilities.control import MinWarning
-from utilities.model_arch_info import get_input_shape
-from utilities.model_arch_info import get_num_classes
from utilities.utilities import _assert
from utilities.utilities import is_platform_pg
from utilities.utilities import get_segments_per_host
from utilities.utilities import madlib_version
-from utilities.validate_args import get_col_value_and_type
from utilities.validate_args import get_expr_type
from utilities.validate_args import quote_ident
from utilities.control import MinWarning
@@ -68,7 +62,7 @@ def fit(schema_madlib, source_table, model, model_arch_table,
fit_validator = FitInputValidator(
source_table, validation_table, model, model_arch_table,
- mb_dep_var_col, mb_indep_var_col,
+ model_arch_id, mb_dep_var_col, mb_indep_var_col,
num_iterations, metrics_compute_frequency, warm_start)
if metrics_compute_frequency is None:
metrics_compute_frequency = num_iterations
@@ -88,23 +82,13 @@ def fit(schema_madlib, source_table, model,
model_arch_table,
# Get the serialized master model
start_deserialization = time.time()
- model_arch_query = "SELECT {0}, {1} FROM {2} WHERE {3} = {4}".format(
- ModelArchSchema.MODEL_ARCH, ModelArchSchema.MODEL_WEIGHTS,
- model_arch_table, ModelArchSchema.MODEL_ID,
- model_arch_id)
- model_arch_result = plpy.execute(model_arch_query)
- if not model_arch_result:
- plpy.error("no model arch found in table {0} with id {1}".format(
- model_arch_table, model_arch_id))
- model_arch_result = model_arch_result[0]
- model_arch = model_arch_result[ModelArchSchema.MODEL_ARCH]
- input_shape = get_input_shape(model_arch)
+ model_arch, model_weights = get_model_arch_weights(model_arch_table,
model_arch_id)
num_classes = get_num_classes(model_arch)
+ input_shape = get_input_shape(model_arch)
fit_validator.validate_input_shapes(input_shape)
-
gp_segment_id_col = '0' if is_platform_pg() else 'gp_segment_id'
- serialized_weights = get_initial_weights(model, model_arch_result,
+ serialized_weights = get_initial_weights(model, model_arch, model_weights,
warm_start, gpus_per_host)
# Compute total images on each segment
seg_ids_train, images_per_seg_train =
get_image_count_per_seg_for_minibatched_data_from_db(source_table)
@@ -289,7 +273,7 @@ def fit(schema_madlib, source_table, model,
model_arch_table,
#TODO add a unit test for this in a future PR
reset_cuda_env(original_cuda_env)
-def get_initial_weights(model_table, model_arch_result, warm_start,
gpus_per_host):
+def get_initial_weights(model_table, model_arch, serialized_weights,
warm_start, gpus_per_host):
"""
If warm_start is True, return back initial weights from model table.
If warm_start is False, first try to get the weights from model_arch
@@ -317,10 +301,8 @@ def get_initial_weights(model_table, model_arch_result,
warm_start, gpus_per_hos
SELECT model_data FROM {0}
""".format(model_table))[0]['model_data']
else:
- serialized_weights = model_arch_result[ModelArchSchema.MODEL_WEIGHTS]
if not serialized_weights:
- model = model_from_json(
- model_arch_result[ModelArchSchema.MODEL_ARCH])
+ model = model_from_json(model_arch)
serialized_weights = madlib_keras_serializer.serialize_nd_weights(
model.get_weights())
return serialized_weights
@@ -518,10 +500,12 @@ def get_segments_and_gpus(gpus_per_host):
def evaluate(schema_madlib, model_table, test_table, output_table,
gpus_per_host, **kwargs):
module_name = 'madlib_keras_evaluate'
- input_validator = EvaluateInputValidator(test_table, model_table,
output_table, module_name)
-
- model_summary_table = input_validator.model_summary_table
- test_summary_table = input_validator.test_summary_table
+ if test_table:
+ test_summary_table = add_postfix(test_table, "_summary")
+ model_summary_table = None
+ if model_table:
+ model_summary_table = add_postfix(model_table, "_summary")
+ validate_evaluate(module_name, model_table, model_summary_table,
test_table, test_summary_table, output_table)
segments_per_host, gpus_per_host = get_segments_and_gpus(gpus_per_host)
@@ -531,7 +515,8 @@ def evaluate(schema_madlib, model_table, test_table,
output_table, gpus_per_host
model_arch = res['model_arch']
input_shape = get_input_shape(model_arch)
- input_validator.validate_input_shape(input_shape)
+ InputValidator.validate_input_shape(
+ test_table, MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL, input_shape, 2)
compile_params_query = "SELECT compile_params, metrics_type FROM
{0}".format(model_summary_table)
res = plpy.execute(compile_params_query)[0]
@@ -540,10 +525,11 @@ def evaluate(schema_madlib, model_table, test_table,
output_table, gpus_per_host
seg_ids, images_per_seg =
get_image_count_per_seg_for_minibatched_data_from_db(test_table)
- loss, metric =\
- get_loss_metric_from_keras_eval(schema_madlib, test_table,
compile_params, model_arch,
- model_data, gpus_per_host,
segments_per_host,
- seg_ids, images_per_seg)
+ loss, metric = \
+ get_loss_metric_from_keras_eval(
+ schema_madlib, test_table, compile_params, model_arch,
+ model_data, gpus_per_host, segments_per_host,
+ seg_ids, images_per_seg)
if not metrics_type:
metrics_type = None
@@ -555,6 +541,23 @@ def evaluate(schema_madlib, model_table, test_table,
output_table, gpus_per_host
SELECT $1 as loss, $2 as metric, $3 as
metrics_type""".format(output_table), ["FLOAT", "FLOAT", "TEXT[]"])
plpy.execute(create_output_table, [loss, metric, metrics_type])
+def validate_evaluate(module_name, model_table, model_summary_table,
test_table, test_summary_table, output_table):
+ def _validate_test_summary_tbl():
+ input_tbl_valid(test_summary_table, module_name,
+ error_suffix_str="Please ensure that the test table ({0}) "
+ "has been preprocessed by "
+ "the image preprocessor.".format(test_table))
+ cols_in_tbl_valid(test_summary_table, [CLASS_VALUES_COLNAME,
+ NORMALIZING_CONST_COLNAME, DEPENDENT_VARTYPE_COLNAME,
+ DEPENDENT_VARNAME_COLNAME, INDEPENDENT_VARNAME_COLNAME],
module_name)
+
+ InputValidator.validate_predict_evaluate_tables(
+ module_name, model_table, model_summary_table,
+ test_table, output_table, MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL)
+ _validate_test_summary_tbl()
+ validate_dependent_var_for_minibatch(test_table,
+ MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL)
+
def get_loss_metric_from_keras_eval(schema_madlib, table, compile_params,
model_arch, serialized_weights,
gpus_per_host,
segments_per_host, seg_ids,
images_per_seg):
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras.sql_in
b/src/ports/postgres/modules/deep_learning/madlib_keras.sql_in
index c5d8d35..c69f158 100644
--- a/src/ports/postgres/modules/deep_learning/madlib_keras.sql_in
+++ b/src/ports/postgres/modules/deep_learning/madlib_keras.sql_in
@@ -35,6 +35,7 @@ m4_include(`SQLCommon.m4')
<li class="level1"><a href="#keras_fit">Fit</a></li>
<li class="level1"><a href="#keras_evaluate">Evaluate</a></li>
<li class="level1"><a href="#keras_predict">Predict</a></li>
+<li class="level1"><a href="#keras_predict_byom">Predict BYOM</a></li>
<li class="level1"><a href="#example">Examples</a></li>
<li class="level1"><a href="#notes">Notes</a></li>
<li class="level1"><a href="#background">Technical Background</a></li>
@@ -616,6 +617,127 @@ madlib_keras_predict(
</DD>
</DL>
+
+
+@anchor keras_predict_byom
+@par Predict BYOM (Bring your own model)
+The predict byom function has the following format:
+<pre class="syntax">
+madlib_keras_predict_byom(
+ model_arch_table,
+ model_arch_id,
+ test_table,
+ id_col,
+ independent_varname,
+ output_table,
+ pred_type,
+ gpus_per_host,
+ class_values,
+ normalizing_const
+ )
+</pre>
+
+
+\b Arguments
+<dl class="arglist">
+
+<DT>model_arch_table</DT>
+ <DD>TEXT. Name of the architecture table containing the model
+ to use for prediction. The model weights and architecture can be loaded to
+ this table by using the
+ <a href="group__grp__keras__model__arch.html">load_keras_model</a> function
+ </DD>
+
+ <DT>model_arch_id</DT>
+ <DD>INTEGER. This is the id in 'model_arch_table'containing the model
+ architecture and model weights to use for prediction.
+ </DD>
+
+ <DT>test_table</DT>
+ <DD>TEXT. Name of the table containing the dataset to
+ predict on. Note that test data is not preprocessed (unlike
+ fit and evaluate) so put one test image per row for prediction.
+ Also see the comment below for the 'independent_varname' parameter
+ regarding normalization.
+
+ </DD>
+
+ <DT>id_col</DT>
+ <DD>TEXT. Name of the id column in the test data table.
+ </DD>
+
+ <DT>independent_varname</DT>
+ <DD>TEXT. Column with independent variables in the test table.
+ If a 'normalizing_const' is specified when preprocessing the
+ training dataset, this same normalization will be applied to
+ the independent variables used in predict.
+ </DD>
+
+ <DT>output_table</DT>
+ <DD>TEXT. Name of the table that prediction output will be
+ written to. Table contains:</DD>
+ <table class="output">
+ <tr>
+ <th>id</th>
+ <td>Gives the 'id' for each prediction, corresponding to each row from
the test_table.</td>
+ </tr>
+ <tr>
+ <th>estimated_dependent_var</th>
+ <td>
+ (For pred_type='response') The estimated class for classification. If
+ class_values is passed in as NULL, then we assume that the class
+ labels are [0,1,2...,n] where n in the num of classes in the model
+ architecture.
+ </td>
+ </tr>
+ <tr>
+ <th>prob_CLASS</th>
+ <td>
+ (For pred_type='prob' for classification)
+ The probability of a given class.
+ If class_values is passed in as NULL, we create just one column called
+ 'prob' which is an array of probabilities of all the classes.
+ Otherwise if class_values is not NULL, then there will be one
+ column for each class in the training data.
+ </td>
+ </tr>
+
+ <DT>pred_type (optional)</DT>
+ <DD>TEXT, default: 'response'. The type of output desired, where 'response'
+ gives the actual prediction and 'prob' gives the probability value for each
class.
+ </DD>
+
+ <DT>gpus_per_host (optional)</DT>
+ <DD>INTEGER, default: 0 (i.e., CPU).
+ Number of GPUs per segment host to be used for training the neural network.
+ For example, if you specify 4 for this parameter and your database cluster
+ is set up to have 4 segments per segment host, it means that each segment
+ will have a dedicated GPU. A value of 0 means that CPUs, not GPUs, will
+ be used for training.
+
+ @note
+ We have seen some memory related issues when segments share GPU resources.
+ For example, if you specify 1 for this parameter and your database cluster
+ is set up to have 4 segments per segment host, it means that all 4 segments
+ on a segment host will share the same GPU. The current recommended
+ configuration is 1 GPU per segment.
+ </DD>
+
+ <DT>class_values (optional)</DT>
+ <DD>TEXT[], default: NULL.
+ List of class labels that were used while training the model. See the
+ output_table column for more details.
+ </DD>
+
+ <DT>normalizing_const (optional)</DT>
+ <DD>DOUBLE PRECISION, default: 1.0.
+ The normalizing constant to divide each value in the 'independent_varname'
+ array by. For example, you would use 255 for this value if the image data is
+ in the form 0-255.
+ </DD>
+</DL>
+
+
@anchor example
@par Examples
@@ -814,7 +936,6 @@ SELECT COUNT(*) FROM iris_train;
-# Call the preprocessor for deep learning. For the training dataset:
<pre class="example">
-DROP TABLE IF EXISTS mlp_prediction;
\\x off
DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;
SELECT madlib.training_preprocessor_dl('iris_train', -- Source table
@@ -1049,6 +1170,142 @@ WHERE q.actual=q.estimated;
(1 row)
</pre>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+-# Predict BYOM.
+We will use the validation dataset for prediction
+as well, which is not usual but serves to show the
+syntax. See <a href="group__grp__keras__model__arch.html">load_keras_model</a>
+for details on how to load the model architecture and weights.
+
+
+The prediction is in the 'estimated_dependent_var'
+column:
+<pre class="example">
+UPDATE model_arch_library set model_weights = (select model_data from
iris_model) WHERE model_id = 1;
+
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib.madlib_keras_predict_byom('model_arch_library', -- model arch
table
+ 1, -- model arch id
+ 'iris_test', -- test_table
+ 'id', -- id column
+ 'attributes', -- independent var
+ 'iris_predict_byom', -- output table
+ 'response', -- pred_type
+ 0, -- gpus_per_host
+ ARRAY['Iris-setosa', 'Iris-versicolor',
+ 'Iris-virginica'], -- class_values
+ 1.0 -- normalizing_const
+ );
+SELECT * FROM iris_predict_byom ORDER BY id;
+</pre>
+<pre class="result">
+ id | estimated_dependent_var
+-----+-------------------------
+ 1 | Iris-setosa
+ 4 | Iris-setosa
+ 9 | Iris-setosa
+ 27 | Iris-setosa
+ 32 | Iris-setosa
+ 35 | Iris-setosa
+ 40 | Iris-setosa
+ 41 | Iris-setosa
+ 44 | Iris-setosa
+ 46 | Iris-setosa
+ 55 | Iris-versicolor
+ 56 | Iris-versicolor
+ 66 | Iris-versicolor
+ 69 | Iris-versicolor
+ 75 | Iris-versicolor
+ 76 | Iris-versicolor
+ 102 | Iris-virginica
+ 105 | Iris-virginica
+ 108 | Iris-virginica
+ 113 | Iris-virginica
+ 115 | Iris-virginica
+ 116 | Iris-virginica
+ 118 | Iris-virginica
+ 119 | Iris-virginica
+ 122 | Iris-virginica
+ 125 | Iris-virginica
+ 133 | Iris-virginica
+ 134 | Iris-virginica
+ 135 | Iris-virginica
+ 138 | Iris-virginica
+ </pre>
+Count missclassifications:
+<pre class="example">
+SELECT COUNT(*) FROM iris_predict_byom JOIN iris_test USING (id)
+WHERE iris_predict_byom.estimated_dependent_var != iris_test.class_text;
+</pre>
+<pre class="result">
+ count
+-------+
+ 6
+(1 row)
+</pre>
+Percent missclassifications:
+<pre class="example">
+SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from
+ (select iris_test.class_text as actual,
iris_predict_byom.estimated_dependent_var as estimated
+ from iris_predict_byom inner join iris_test
+ on iris_test.id=iris_predict_byom.id) q
+WHERE q.actual=q.estimated;
+</pre>
+<pre class="result">
+ test_accuracy_percent
+-----------------------+
+ 80.00
+(1 row)
+</pre>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
<h4>Classification with Other Parameters</h4>
-# Validation dataset. Now use a validation dataset
@@ -1571,7 +1828,7 @@ CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.madlib_keras_predict(
) RETURNS VOID AS $$
PythonFunctionBodyOnly(`deep_learning', `madlib_keras_predict')
with AOControl(False):
- madlib_keras_predict.predict(schema_madlib,
+ madlib_keras_predict.Predict(schema_madlib,
model_table,
test_table,
id_col,
@@ -1622,6 +1879,82 @@ CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.internal_keras_predict(
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+-------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
+ model_arch_table VARCHAR,
+ model_arch_id INTEGER,
+ test_table VARCHAR,
+ id_col VARCHAR,
+ independent_varname VARCHAR,
+ output_table VARCHAR,
+ pred_type VARCHAR,
+ gpus_per_host INTEGER,
+ class_values TEXT[],
+ normalizing_const DOUBLE PRECISION
+) RETURNS VOID AS $$
+ PythonFunctionBodyOnly(`deep_learning', `madlib_keras_predict')
+ with AOControl(False):
+ madlib_keras_predict.PredictBYOM(**globals())
+$$ LANGUAGE plpythonu VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
+ model_arch_table VARCHAR,
+ model_arch_id INTEGER,
+ test_table VARCHAR,
+ id_col VARCHAR,
+ independent_varname VARCHAR,
+ output_table VARCHAR,
+ pred_type VARCHAR,
+ gpus_per_host INTEGER,
+ class_values TEXT[]
+) RETURNS VOID AS $$
+ SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6, $7,
$8, $9, NULL);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
+ model_arch_table VARCHAR,
+ model_arch_id INTEGER,
+ test_table VARCHAR,
+ id_col VARCHAR,
+ independent_varname VARCHAR,
+ output_table VARCHAR,
+ pred_type VARCHAR,
+ gpus_per_host INTEGER
+) RETURNS VOID AS $$
+ SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6, $7,
$8, NULL, NULL);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
+ model_arch_table VARCHAR,
+ model_arch_id INTEGER,
+ test_table VARCHAR,
+ id_col VARCHAR,
+ independent_varname VARCHAR,
+ output_table VARCHAR,
+ pred_type VARCHAR
+) RETURNS VOID AS $$
+ SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6, $7,
NULL, NULL, NULL);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
+ model_arch_table VARCHAR,
+ model_arch_id INTEGER,
+ test_table VARCHAR,
+ id_col VARCHAR,
+ independent_varname VARCHAR,
+ output_table VARCHAR
+) RETURNS VOID AS $$
+ SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6,
NULL, NULL, NULL, NULL);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+-------------------------------------------------------------------------------
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_evaluate(
model_table VARCHAR,
test_table VARCHAR,
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
b/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
index 17bdda4..e8218a6 100644
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
+++ b/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
@@ -45,6 +45,8 @@ MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL = "independent_var"
FLOAT32_SQL_TYPE = 'REAL'
SMALLINT_SQL_TYPE = 'SMALLINT'
+DEFAULT_NORMALIZING_CONST = 1.0
+
#####################################################################
# Prepend a dimension to np arrays using expand_dims.
diff --git
a/src/ports/postgres/modules/deep_learning/madlib_keras_predict.py_in
b/src/ports/postgres/modules/deep_learning/madlib_keras_predict.py_in
index ca7a9ad..819ff98 100644
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_predict.py_in
+++ b/src/ports/postgres/modules/deep_learning/madlib_keras_predict.py_in
@@ -18,7 +18,6 @@
# under the License.
import plpy
-import os
import keras
from keras import backend as K
@@ -26,82 +25,190 @@ from keras.layers import *
from keras.models import *
from keras.optimizers import *
+from model_arch_info import *
from madlib_keras_helper import *
-from madlib_keras_validator import PredictInputValidator
+from madlib_keras_validator import *
from predict_input_params import PredictParamsProcessor
from utilities.control import MinWarning
-from utilities.model_arch_info import get_input_shape
+from utilities.utilities import _assert
from utilities.utilities import add_postfix
from utilities.utilities import create_cols_from_array_sql_string
from utilities.utilities import get_segments_per_host
-from utilities.utilities import is_platform_pg
from utilities.utilities import unique_string
+from utilities.validate_args import input_tbl_valid
+from utilities.validate_args import quote_ident
from madlib_keras_wrapper import *
-MODULE_NAME = 'madlib_keras_predict'
+class BasePredict():
+ def __init__(self, schema_madlib, table_to_validate, test_table, id_col,
+ independent_varname, output_table, pred_type, gpus_per_host):
+ self.schema_madlib = schema_madlib
+ self.table_to_validate = table_to_validate
+ self.test_table = test_table
+ self.id_col = id_col
+ self.independent_varname = independent_varname
+ self.output_table = output_table
+ self.pred_type = pred_type
+ self.gpus_per_host = gpus_per_host
+ self._set_default_gpus_pred_type()
+
+ def _set_default_gpus_pred_type(self):
+ self.pred_type = 'response' if not self.pred_type else self.pred_type
+ self.is_response = True if self.pred_type == 'response' else False
+ self.gpus_per_host = 0 if self.gpus_per_host is None else
self.gpus_per_host
+
+
+ def call_internal_keras(self):
+ if self.is_response:
+ pred_col_name = add_postfix("estimated_", self.dependent_varname)
+ pred_col_type = self.dependent_vartype
+ else:
+ pred_col_name = "prob"
+ pred_col_type = 'double precision'
+
+ intermediate_col = unique_string()
+ class_values =
strip_trailing_nulls_from_class_values(self.class_values)
+
+ prediction_select_clause = create_cols_from_array_sql_string(
+ class_values, intermediate_col, pred_col_name,
+ pred_col_type, self.is_response, self.module_name)
+ gp_segment_id_col, seg_ids_test, \
+ images_per_seg_test =
get_image_count_per_seg_for_non_minibatched_data_from_db(
+ self.test_table)
+ segments_per_host = get_segments_per_host()
+
+ predict_query = plpy.prepare("""
+ CREATE TABLE {self.output_table} AS
+ SELECT {self.id_col}, {prediction_select_clause}
+ FROM (
+ SELECT {self.test_table}.{self.id_col},
+ ({self.schema_madlib}.internal_keras_predict
+ ({self.independent_varname},
+ $1,
+ $2,
+ {self.is_response},
+ {self.normalizing_const},
+ {gp_segment_id_col},
+ ARRAY{seg_ids_test},
+ ARRAY{images_per_seg_test},
+ {self.gpus_per_host},
+ {segments_per_host})
+ ) AS {intermediate_col}
+ FROM {self.test_table}
+ ) q
+ """.format(self=self,
prediction_select_clause=prediction_select_clause,
+ seg_ids_test=seg_ids_test,
+ images_per_seg_test=images_per_seg_test,
+ gp_segment_id_col=gp_segment_id_col,
+ segments_per_host=segments_per_host,
+ intermediate_col=intermediate_col),
+ ["text", "bytea"])
+ plpy.execute(predict_query, [self.model_arch, self.model_weights])
+
+ def set_default_class_values(self, class_values):
+ self.class_values = class_values
+ if self.pred_type == 'prob':
+ return
+ if self.class_values is None:
+ num_classes = get_num_classes(self.model_arch)
+ self.class_values = range(0, num_classes)
@MinWarning("warning")
-def predict(schema_madlib, model_table, test_table, id_col,
- independent_varname, output_table, pred_type, gpus_per_host,
**kwargs):
- if not pred_type:
- pred_type = 'response'
- input_validator = PredictInputValidator(
- test_table, model_table, id_col, independent_varname,
- output_table, pred_type, MODULE_NAME)
-
- param_proc = PredictParamsProcessor(model_table, MODULE_NAME)
- class_values = param_proc.get_class_values()
- input_validator.validate_pred_type(class_values)
- dependent_varname = param_proc.get_dependent_varname()
- dependent_vartype = param_proc.get_dependent_vartype()
- model_data = param_proc.get_model_data()
- model_arch = param_proc.get_model_arch()
- normalizing_const = param_proc.get_normalizing_const()
- input_shape = get_input_shape(model_arch)
- input_validator.validate_input_shape(input_shape)
-
- is_response = True if pred_type == 'response' else False
- intermediate_col = unique_string()
- if is_response:
- pred_col_name = add_postfix("estimated_", dependent_varname)
- pred_col_type = dependent_vartype
- else:
- pred_col_name = "prob"
- pred_col_type = 'double precision'
-
- class_values = strip_trailing_nulls_from_class_values(class_values)
-
- prediction_select_clause = create_cols_from_array_sql_string(
- class_values, intermediate_col, pred_col_name,
- pred_col_type, is_response, MODULE_NAME)
-
- gp_segment_id_col, seg_ids_test, \
- images_per_seg_test =
get_image_count_per_seg_for_non_minibatched_data_from_db(test_table)
- segments_per_host = get_segments_per_host()
-
- predict_query = plpy.prepare("""
- CREATE TABLE {output_table} AS
- SELECT {id_col}, {prediction_select_clause}
- FROM (
- SELECT {test_table}.{id_col},
- ({schema_madlib}.internal_keras_predict
- ({independent_varname},
- $1,
- $2,
- {is_response},
- {normalizing_const},
- {gp_segment_id_col},
- ARRAY{seg_ids_test},
- ARRAY{images_per_seg_test},
- {gpus_per_host},
- {segments_per_host})
- ) AS {intermediate_col}
- FROM {test_table}
- ) q
- """.format(**locals()), ["text", "bytea"])
- plpy.execute(predict_query, [model_arch, model_data])
+class Predict(BasePredict):
+ def __init__(self, schema_madlib, model_table,
+ test_table, id_col, independent_varname,
+ output_table, pred_type, gpus_per_host,
+ **kwargs):
+
+ self.module_name = 'madlib_keras_predict'
+ self.model_table = model_table
+ if self.model_table:
+ self.model_summary_table = add_postfix(self.model_table,
"_summary")
+
+ BasePredict.__init__(self, schema_madlib, model_table, test_table,
+ id_col, independent_varname,
+ output_table, pred_type,
+ gpus_per_host)
+ param_proc = PredictParamsProcessor(model_table, self.module_name)
+ self.dependent_vartype = param_proc.get_dependent_vartype()
+ self.model_weights = param_proc.get_model_data()
+ self.model_arch = param_proc.get_model_arch()
+ class_values = param_proc.get_class_values()
+ self.set_default_class_values(class_values)
+ self.normalizing_const = param_proc.get_normalizing_const()
+ self.dependent_varname = param_proc.get_dependent_varname()
+
+ self.validate()
+ BasePredict.call_internal_keras(self)
+
+ def validate(self):
+ InputValidator.validate_predict_evaluate_tables(
+ self.module_name, self.model_table, self.model_summary_table,
+ self.test_table, self.output_table, self.independent_varname)
+
+ InputValidator.validate_id_in_test_tbl(
+ self.module_name, self.test_table, self.id_col)
+
+ InputValidator.validate_class_values(
+ self.module_name, self.class_values, self.pred_type,
self.model_arch)
+ input_shape = get_input_shape(self.model_arch)
+ InputValidator.validate_pred_type(
+ self.module_name, self.pred_type, self.class_values)
+ InputValidator.validate_input_shape(
+ self.test_table, self.independent_varname, input_shape, 1)
+
+@MinWarning("warning")
+class PredictBYOM(BasePredict):
+ def __init__(self, schema_madlib, model_arch_table, model_arch_id,
+ test_table, id_col, independent_varname, output_table,
+ pred_type, gpus_per_host, class_values, normalizing_const,
+ **kwargs):
+ self.module_name='madlib_keras_predict_byom'
+ self.model_arch_table = model_arch_table
+ self.model_arch_id = model_arch_id
+ self.class_values = class_values
+ self.normalizing_const = normalizing_const
+ self.dependent_varname = 'dependent_var'
+ BasePredict.__init__(self, schema_madlib, model_arch_table,
+ test_table, id_col, independent_varname,
+ output_table, pred_type, gpus_per_host)
+ if self.is_response:
+ self.dependent_vartype = 'text'
+ else:
+ self.dependent_vartype = 'double precision'
+ ## Set default values for norm const and class_values
+ # gpus_per_host and pred_type are defaulted in base_predict's init
+ self.normalizing_const = normalizing_const
+ if self.normalizing_const is None:
+ self.normalizing_const = DEFAULT_NORMALIZING_CONST
+ InputValidator.validate_predict_byom_tables(
+ self.module_name, self.model_arch_table, self.model_arch_id,
+ self.test_table, self.id_col, self.output_table,
+ self.independent_varname)
+ self.validate_and_set_defaults()
+ BasePredict.call_internal_keras(self)
+
+ def validate_and_set_defaults(self):
+ # Set some defaults first and then validate and then set some more
defaults
+ self.model_arch, self.model_weights = get_model_arch_weights(
+ quote_ident(self.model_arch_table), self.model_arch_id)
+ # Assert model_weights and model_arch are not empty.
+ _assert(self.model_weights and self.model_arch,
+ "{0}: Model weights and architecture should not be
NULL.".format(
+ self.module_name))
+ self.set_default_class_values(self.class_values)
+
+ InputValidator.validate_pred_type(
+ self.module_name, self.pred_type, self.class_values)
+ InputValidator.validate_normalizing_const(
+ self.module_name, self.normalizing_const)
+ InputValidator.validate_class_values(
+ self.module_name, self.class_values, self.pred_type,
self.model_arch)
+ InputValidator.validate_input_shape(
+ self.test_table, self.independent_varname,
+ get_input_shape(self.model_arch), 1)
def internal_keras_predict(independent_var, model_architecture, model_data,
is_response, normalizing_const, current_seg_id,
seg_ids,
@@ -216,9 +323,86 @@ estimated_COL_NAME: (For pred_type='response') The
estimated class for
prob_CLASS: (For pred_type='prob' for classification) The
probability of a given class. There will be one
column for each class in the training data.
+ TODO change this
"""
else:
help_string = "No such option. Use
{schema_madlib}.madlib_keras_predict()"
return help_string.format(schema_madlib=schema_madlib)
+
+def predict_byom_help(schema_madlib, message, **kwargs):
+ """
+ Help function for keras predict
+
+ Args:
+ @param schema_madlib
+ @param message: string, Help message string
+ @param kwargs
+
+ Returns:
+ String. Help/usage information
+ """
+ if not message:
+ help_string = """
+-----------------------------------------------------------------------
+ SUMMARY
+-----------------------------------------------------------------------
+This function allows the user to predict with their own pre trained model (note
+that this model doesn't have to be trained using MADlib.)
+
+For more details on function usage:
+ SELECT {schema_madlib}.madlib_keras_predict_byom('usage')
+ """
+ elif message in ['usage', 'help', '?']:
+ help_string = """
+-----------------------------------------------------------------------
+ USAGE
+-----------------------------------------------------------------------
+ SELECT {schema_madlib}.madlib_keras_predict_byom(
+ model_arch_table, -- Name of the table containing the model architecture
+ and the pre trained model weights
+ model_arch_id, -- This is the id in 'model_arch_table' containing the
+ model architecture
+ test_table, -- Name of the table containing the evaluation dataset
+ id_col, -- Name of the id column in the test data table
+ independent_varname, -- Name of the column with independent
+ variables in the test table
+ output_table, -- Name of the output table
+ pred_type, -- The type of the desired output
+ gpus_per_host, -- Number of GPUs per segment host to
+ be used for training
+ class_values, -- List of class labels that were used while training
the
+ model. If class_values is passed in as NULL, the
output
+ table will have a column named 'prob' which is an
array
+ of probabilities of all the classes.
+ Otherwise if class_values is not NULL, then the output
+ table will contain a column for each class/label from
+ the training data
+ normalizing_const -- Normalizing constant used for standardizing arrays in
+ independent_varname
+ )
+ );
+
+-----------------------------------------------------------------------
+ OUTPUT
+-----------------------------------------------------------------------
+The output table ('output_table' above) contains the following columns:
+
+id: Gives the 'id' for each prediction, corresponding
+ to each row from the test_table.
+estimated_dependent_var: (For pred_type='response') The estimated class for
+ classification. If class_values is passed in as NULL, then
we
+ assume that the class labels are [0,1,2...,n] where n in
the
+ num of classes in the model architecture.
+prob_CLASS: (For pred_type='prob' for classification) The
+ probability of a given class.
+ If class_values is passed in as NULL, we create just one
column
+ called 'prob' which is an array of probabilites of all the
classes
+ Otherwise if class_values is not NULL, then there will be
one
+ column for each class in the training data.
+"""
+ else:
+ help_string = "No such option. Use
{schema_madlib}.madlib_keras_predict_byom()"
+
+ return help_string.format(schema_madlib=schema_madlib)
# ---------------------------------------------------------------------
diff --git
a/src/ports/postgres/modules/deep_learning/madlib_keras_validator.py_in
b/src/ports/postgres/modules/deep_learning/madlib_keras_validator.py_in
index b111fc4..e9d7d14 100644
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_validator.py_in
+++ b/src/ports/postgres/modules/deep_learning/madlib_keras_validator.py_in
@@ -19,6 +19,7 @@
import plpy
from keras_model_arch_table import ModelArchSchema
+from model_arch_info import get_input_shape, get_num_classes
from madlib_keras_helper import CLASS_VALUES_COLNAME
from madlib_keras_helper import COMPILE_PARAMS_COLNAME
from madlib_keras_helper import DEPENDENT_VARNAME_COLNAME
@@ -45,182 +46,177 @@ from utilities.validate_args import get_expr_type
from utilities.validate_args import input_tbl_valid
from utilities.validate_args import output_tbl_valid
-
-def _validate_input_shapes(table, independent_varname, input_shape, offset):
- """
- Validate if the input shape specified in model architecture is the same
- as the shape of the image specified in the indepedent var of the input
- table.
- offset: This offset is the index of the start of the image array. We also
- need to consider that sql array indexes start from 1
- For ex if the image is of shape [32,32,3] and is minibatched, the image
will
- look like [10, 32, 32, 3]. The offset in this case is 1 (start the index
at 1) +
- 1 (ignore the buffer size 10) = 2.
- If the image is not batched then it will look like [32, 32 ,3] and the
offset in
- this case is 1 (start the index at 1).
- """
- array_upper_query = ", ".join("array_upper({0}, {1}) AS n_{2}".format(
- independent_varname, i+offset, i) for i in range(len(input_shape)))
- query = """
- SELECT {0}
- FROM {1}
- LIMIT 1
- """.format(array_upper_query, table)
- # This query will fail if an image in independent var does not have the
- # same number of dimensions as the input_shape.
- result = plpy.execute(query)[0]
- _assert(len(result) == len(input_shape),
- "model_keras error: The number of dimensions ({0}) of each image"
- " in model architecture and {1} in {2} ({3}) do not match.".format(
- len(input_shape), independent_varname, table, len(result)))
- for i in range(len(input_shape)):
- key_name = "n_{0}".format(i)
- if result[key_name] != input_shape[i]:
- # Construct the shape in independent varname to display
- # meaningful error msg.
- input_shape_from_table = [result["n_{0}".format(i)]
- for i in range(len(input_shape))]
- plpy.error("model_keras error: Input shape {0} in the model"
- " architecture does not match the input shape {1} of column"
- " {2} in table {3}.".format(
- input_shape, input_shape_from_table,
- independent_varname, table))
-
class InputValidator:
- def __init__(self, test_table, model_table, independent_varname,
- output_table, module_name):
- self.test_table = test_table
- self.model_table = model_table
- self.independent_varname = independent_varname
- self.output_table = output_table
- if self.model_table:
- self.model_summary_table = add_postfix(
- self.model_table, "_summary")
- self.module_name = module_name
- self._validate_input_args()
-
- def _validate_input_args(self):
- input_tbl_valid(self.model_table, self.module_name)
- self._validate_model_data_cols()
- input_tbl_valid(self.model_summary_table, self.module_name)
- self._validate_model_summary_tbl_cols()
- input_tbl_valid(self.test_table, self.module_name)
- self._validate_test_tbl_cols()
- output_tbl_valid(self.output_table, self.module_name)
-
-
- def _validate_model_data_cols(self):
- _assert(is_var_valid(self.model_table, MODEL_DATA_COLNAME),
+ @staticmethod
+ def validate_predict_evaluate_tables(
+ module_name, model_table, model_summary_table, test_table,
output_table,
+ independent_varname):
+ InputValidator._validate_model_data_tbl(module_name, model_table)
+ InputValidator._validate_model_summary_tbl(
+ module_name, model_summary_table)
+ InputValidator._validate_test_tbl(
+ module_name, test_table, independent_varname)
+ output_tbl_valid(output_table, module_name)
+
+ @staticmethod
+ def validate_id_in_test_tbl(module_name, test_table, id_col):
+ _assert(is_var_valid(test_table, id_col),
+ "{module_name} error: invalid id column "
+ "('{id_col}') for test table ({table}).".format(
+ module_name=module_name,
+ id_col=id_col,
+ table=test_table))
+
+ @staticmethod
+ def validate_predict_byom_tables(module_name, model_arch_table,
model_arch_id,
+ test_table, id_col, output_table,
+ independent_varname):
+ InputValidator.validate_model_arch_table(
+ module_name, model_arch_table, model_arch_id)
+ InputValidator._validate_test_tbl(
+ module_name, test_table, independent_varname)
+ InputValidator.validate_id_in_test_tbl(module_name, test_table, id_col)
+
+ output_tbl_valid(output_table, module_name)
+
+
+ @staticmethod
+ def validate_pred_type(module_name, pred_type, class_values):
+ if not pred_type in ['prob', 'response']:
+ plpy.error("{0}: Invalid value for pred_type param ({1}). Must be
"\
+ "either response or prob.".format(module_name, pred_type))
+
+
+ @staticmethod
+ def validate_input_shape(table, independent_varname, input_shape, offset):
+ """
+ Validate if the input shape specified in model architecture is the same
+ as the shape of the image specified in the indepedent var of the input
+ table.
+ offset: This offset is the index of the start of the image array. We
also
+ need to consider that sql array indexes start from 1
+ For ex if the image is of shape [32,32,3] and is minibatched, the
image will
+ look like [10, 32, 32, 3]. The offset in this case is 1 (start the
index at 1) +
+ 1 (ignore the buffer size 10) = 2.
+ If the image is not batched then it will look like [32, 32 ,3] and the
offset in
+ this case is 1 (start the index at 1).
+ """
+ array_upper_query = ", ".join("array_upper({0}, {1}) AS n_{2}".format(
+ independent_varname, i+offset, i) for i in range(len(input_shape)))
+ query = """
+ SELECT {0}
+ FROM {1}
+ LIMIT 1
+ """.format(array_upper_query, table)
+ # This query will fail if an image in independent var does not have the
+ # same number of dimensions as the input_shape.
+ result = plpy.execute(query)[0]
+ _assert(len(result) == len(input_shape),
+ "model_keras error: The number of dimensions ({0}) of each image"
+ " in model architecture and {1} in {2} ({3}) do not match.".format(
+ len(input_shape), independent_varname, table, len(result)))
+ for i in range(len(input_shape)):
+ key_name = "n_{0}".format(i)
+ if result[key_name] != input_shape[i]:
+ # Construct the shape in independent varname to display
+ # meaningful error msg.
+ input_shape_from_table = [result["n_{0}".format(i)]
+ for i in range(len(input_shape))]
+ plpy.error("model_keras error: Input shape {0} in the model"
+ " architecture does not match the input shape {1} of
column"
+ " {2} in table {3}.".format(
+ input_shape, input_shape_from_table,
+ independent_varname, table))
+
+ @staticmethod
+ def validate_model_arch_table(module_name, model_arch_table,
model_arch_id):
+ input_tbl_valid(model_arch_table, module_name)
+ _assert(model_arch_id is not None,
+ "{0}: Invalid model architecture ID.".format(module_name))
+
+
+ @staticmethod
+ def validate_normalizing_const(module_name, normalizing_const):
+ _assert(normalizing_const > 0,
+ "{0} error: Normalizing constant has to be greater than 0.".
+ format(module_name))
+
+ @staticmethod
+ def validate_class_values(module_name, class_values, pred_type,
model_arch):
+ if not class_values:
+ return
+ num_classes = len(class_values)
+ _assert(num_classes == get_num_classes(model_arch),
+ "{0}: The number of class values do not match the " \
+ "provided architecture.".format(module_name))
+ if pred_type == 'prob' and num_classes+1 >= 1600:
+ plpy.error({"{0}: The output will have {1} columns, exceeding the
"\
+ " max number of columns that can be created (1600)".format(
+ module_name, num_classes+1)})
+
+ @staticmethod
+ def validate_model_weights(module_name, model_arch, model_weights):
+ _assert(model_weights and model_arch,
+ "{0}: Model weights and architecture must be valid.".format(
+ module_name))
+
+ @staticmethod
+ def _validate_model_data_tbl(module_name, model_table):
+ input_tbl_valid(model_table, module_name)
+ _assert(is_var_valid(model_table, MODEL_DATA_COLNAME),
"{module_name} error: column '{model_data}' "
"does not exist in model table '{table}'.".format(
- module_name=self.module_name,
+ module_name=module_name,
model_data=MODEL_DATA_COLNAME,
- table=self.model_table))
- _assert(is_var_valid(self.model_table, ModelArchSchema.MODEL_ARCH),
+ table=model_table))
+ _assert(is_var_valid(model_table, ModelArchSchema.MODEL_ARCH),
"{module_name} error: column '{model_arch}' "
"does not exist in model table '{table}'.".format(
- module_name=self.module_name,
+ module_name=module_name,
model_arch=ModelArchSchema.MODEL_ARCH,
- table=self.model_table))
+ table=model_table))
- def _validate_test_tbl_cols(self):
- _assert(is_var_valid(self.test_table, self.independent_varname),
+ @staticmethod
+ def _validate_test_tbl(module_name, test_table, independent_varname):
+ input_tbl_valid(test_table, module_name)
+ _assert(is_var_valid(test_table, independent_varname),
"{module_name} error: invalid independent_varname "
"('{independent_varname}') for test table "
"({table}).".format(
- module_name=self.module_name,
- independent_varname=self.independent_varname,
- table=self.test_table))
+ module_name=module_name,
+ independent_varname=independent_varname,
+ table=test_table))
- def _validate_model_summary_tbl_cols(self):
+ @staticmethod
+ def _validate_model_summary_tbl(module_name, model_summary_table):
+ input_tbl_valid(model_summary_table, module_name)
cols_to_check_for = [CLASS_VALUES_COLNAME,
DEPENDENT_VARNAME_COLNAME,
DEPENDENT_VARTYPE_COLNAME,
MODEL_ARCH_ID_COLNAME,
MODEL_ARCH_TABLE_COLNAME,
- NORMALIZING_CONST_COLNAME]
- _assert(columns_exist_in_table(
- self.model_summary_table, cols_to_check_for),
- "{0} error: One or more expected columns missing in model "
- "summary table ('{1}'). The expected columns are {2}.".format(
- self.module_name, self.model_summary_table, cols_to_check_for))
-
-class EvaluateInputValidator(InputValidator):
- def __init__(self, test_table, model_table, output_table, module_name):
- self.test_summary_table = None
- if test_table:
- self.test_summary_table = add_postfix(test_table, "_summary")
-
- self.independent_varname = MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL
- InputValidator.__init__(self, test_table, model_table,
- self.independent_varname,
- output_table, module_name)
-
- def _validate_input_args(self):
- input_tbl_valid(self.test_summary_table, self.module_name,
- error_suffix_str="Please ensure that the test table
({0}) "
- "has been preprocessed by "
- "the image
preprocessor.".format(self.test_table))
- self._validate_test_summary_tbl_cols()
- InputValidator._validate_input_args(self)
- validate_dependent_var_for_minibatch(self.test_table,
-
MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL)
-
- def _validate_model_summary_tbl_cols(self):
- cols_to_check_for = [COMPILE_PARAMS_COLNAME, METRIC_TYPE_COLNAME]
+ NORMALIZING_CONST_COLNAME,
+ COMPILE_PARAMS_COLNAME,
+ METRIC_TYPE_COLNAME]
_assert(columns_exist_in_table(
- self.model_summary_table, cols_to_check_for),
+ model_summary_table, cols_to_check_for),
"{0} error: One or more expected columns missing in model "
"summary table ('{1}'). The expected columns are {2}.".format(
- self.module_name, self.model_summary_table, cols_to_check_for))
+ module_name, model_summary_table, cols_to_check_for))
- def _validate_test_summary_tbl_cols(self):
- cols_in_tbl_valid(self.test_summary_table, [CLASS_VALUES_COLNAME,
- NORMALIZING_CONST_COLNAME, DEPENDENT_VARTYPE_COLNAME,
- DEPENDENT_VARNAME_COLNAME, INDEPENDENT_VARNAME_COLNAME],
self.module_name)
-
- def validate_input_shape(self, input_shape_from_arch):
- _validate_input_shapes(self.test_table, self.independent_varname,
- input_shape_from_arch, 2)
-
-class PredictInputValidator(InputValidator):
- def __init__(self, test_table, model_table, id_col, independent_varname,
- output_table, pred_type, module_name):
- self.id_col = id_col
- self.pred_type = pred_type
- InputValidator.__init__(self, test_table, model_table,
independent_varname,
- output_table, module_name)
-
- def validate_pred_type(self, class_values):
- if not self.pred_type in ['prob', 'response']:
- plpy.error("{0}: Invalid value for pred_type param ({1}). Must be
"\
- "either response or prob.".format(self.module_name,
self.pred_type))
- if self.pred_type == 'prob' and class_values and len(class_values)+1
>= 1600:
- plpy.error({"{0}: The output will have {1} columns, exceeding the
"\
- " max number of columns that can be created (1600)".format(
- self.module_name, len(class_values)+1)})
- def validate_input_shape(self, input_shape_from_arch):
- _validate_input_shapes(self.test_table, self.independent_varname,
- input_shape_from_arch, 1)
- def _validate_test_tbl_cols(self):
- InputValidator._validate_test_tbl_cols(self)
- _assert(is_var_valid(self.test_table, self.id_col),
- "{module_name} error: invalid id column "
- "('{id_col}') for test table ({table}).".format(
- module_name=self.module_name,
- id_col=self.id_col,
- table=self.test_table))
class FitInputValidator:
def __init__(self, source_table, validation_table, output_model_table,
- model_arch_table, dependent_varname, independent_varname,
- num_iterations, metrics_compute_frequency, warm_start):
+ model_arch_table, model_arch_id, dependent_varname,
+ independent_varname, num_iterations,
+ metrics_compute_frequency, warm_start):
self.source_table = source_table
self.validation_table = validation_table
self.output_model_table = output_model_table
self.model_arch_table = model_arch_table
+ self.model_arch_id = model_arch_id
self.dependent_varname = dependent_varname
self.independent_varname = independent_varname
self.metrics_compute_frequency = metrics_compute_frequency
@@ -236,30 +232,6 @@ class FitInputValidator:
self.module_name = 'madlib_keras_fit'
self._validate_input_args()
- def _validate_input_table(self, table):
- _assert(is_var_valid(table, self.independent_varname),
- "{module_name}: invalid independent_varname "
- "('{independent_varname}') for table ({table}). "
- "Please ensure that the input table ({table}) "
- "has been preprocessed by the image preprocessor.".format(
- module_name=self.module_name,
- independent_varname=self.independent_varname,
- table=table))
-
- _assert(is_var_valid(table, self.dependent_varname),
- "{module_name}: invalid dependent_varname "
- "('{dependent_varname}') for table ({table}). "
- "Please ensure that the input table ({table}) "
- "has been preprocessed by the image preprocessor.".format(
- module_name=self.module_name,
- dependent_varname=self.dependent_varname,
- table=table))
-
- def _is_valid_metrics_compute_frequency(self):
- return self.metrics_compute_frequency is None or \
- (self.metrics_compute_frequency >= 1 and \
- self.metrics_compute_frequency <= self.num_iterations)
-
def _validate_input_args(self):
_assert(self.num_iterations > 0,
"{0}: Number of iterations cannot be <
1.".format(self.module_name))
@@ -281,8 +253,8 @@ class FitInputValidator:
self.dependent_varname)
self._validate_validation_table()
-
- input_tbl_valid(self.model_arch_table, self.module_name)
+ InputValidator.validate_model_arch_table(self.module_name,
self.model_arch_table,
+ self.model_arch_id)
if self.warm_start:
input_tbl_valid(self.output_model_table, self.module_name)
input_tbl_valid(self.output_summary_model_table, self.module_name)
@@ -290,6 +262,31 @@ class FitInputValidator:
output_tbl_valid(self.output_model_table, self.module_name)
output_tbl_valid(self.output_summary_model_table, self.module_name)
+ def _validate_input_table(self, table):
+ _assert(is_var_valid(table, self.independent_varname),
+ "{module_name}: invalid independent_varname "
+ "('{independent_varname}') for table ({table}). "
+ "Please ensure that the input table ({table}) "
+ "has been preprocessed by the image preprocessor.".format(
+ module_name=self.module_name,
+ independent_varname=self.independent_varname,
+ table=table))
+
+ _assert(is_var_valid(table, self.dependent_varname),
+ "{module_name}: invalid dependent_varname "
+ "('{dependent_varname}') for table ({table}). "
+ "Please ensure that the input table ({table}) "
+ "has been preprocessed by the image preprocessor.".format(
+ module_name=self.module_name,
+ dependent_varname=self.dependent_varname,
+ table=table))
+
+ def _is_valid_metrics_compute_frequency(self):
+ return self.metrics_compute_frequency is None or \
+ (self.metrics_compute_frequency >= 1 and \
+ self.metrics_compute_frequency <= self.num_iterations)
+
+
def _validate_validation_table(self):
if self.validation_table and self.validation_table.strip() != '':
@@ -305,9 +302,10 @@ class FitInputValidator:
def validate_input_shapes(self, input_shape):
- _validate_input_shapes(self.source_table, self.independent_varname,
+ InputValidator.validate_input_shape(self.source_table,
self.independent_varname,
input_shape, 2)
if self.validation_table:
- _validate_input_shapes(
+ InputValidator.validate_input_shape(
self.validation_table, self.independent_varname,
input_shape, 2)
+
diff --git a/src/ports/postgres/modules/utilities/model_arch_info.py_in
b/src/ports/postgres/modules/deep_learning/model_arch_info.py_in
similarity index 66%
rename from src/ports/postgres/modules/utilities/model_arch_info.py_in
rename to src/ports/postgres/modules/deep_learning/model_arch_info.py_in
index a03594a..c749144 100644
--- a/src/ports/postgres/modules/utilities/model_arch_info.py_in
+++ b/src/ports/postgres/modules/deep_learning/model_arch_info.py_in
@@ -22,6 +22,7 @@ m4_changequote(`<!', `!>')
import sys
import json
import plpy
+from keras_model_arch_table import ModelArchSchema
def _get_layers(model_arch):
d = json.loads(model_arch)
@@ -41,6 +42,22 @@ def get_input_shape(model_arch):
plpy.error('Unable to get input shape from model architecture.')
def get_num_classes(model_arch):
+ """
+ We assume that the last dense layer in the model architecture contains
the num_classes (units)
+ An example can be:
+ ```
+ ...
+ model.add(Flatten())
+ model.add(Dense(512))
+ model.add(Activation('relu'))
+ model.add(Dropout(0.5))
+ model.add(Dense(num_classes))
+ model.add(Activation('softmax'))
+ ```
+ where activation can be after the dense layer.
+ :param model_arch:
+ :return:
+ """
arch_layers = _get_layers(model_arch)
i = len(arch_layers) - 1
while i >= 0:
@@ -66,3 +83,22 @@ def get_model_arch_layers_str(model_arch):
else:
layers += "{1}\n".format(class_name)
return layers
+
+def get_model_arch_weights(model_arch_table, model_arch_id):
+
+ #assume validation is already called
+ model_arch_query = "SELECT {0}, {1} FROM {2} WHERE {3} = {4}".format(
+ ModelArchSchema.MODEL_ARCH, ModelArchSchema.MODEL_WEIGHTS,
+ model_arch_table, ModelArchSchema.MODEL_ID,
+ model_arch_id)
+ model_arch_result = plpy.execute(model_arch_query)
+ if not model_arch_result:
+ plpy.error("no model arch found in table {0} with id {1}".format(
+ model_arch_table, model_arch_id))
+
+ model_arch_result = model_arch_result[0]
+
+ model_arch = model_arch_result[ModelArchSchema.MODEL_ARCH]
+ model_weights = model_arch_result[ModelArchSchema.MODEL_WEIGHTS]
+
+ return model_arch, model_weights
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras.sql_in
b/src/ports/postgres/modules/deep_learning/test/madlib_keras.sql_in
index dacf236..28a500e 100644
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras.sql_in
+++ b/src/ports/postgres/modules/deep_learning/test/madlib_keras.sql_in
@@ -383,21 +383,20 @@ SELECT madlib_keras_predict(
0);
-- Validate that prediction output table exists and has correct schema
-SELECT assert(UPPER(atttypid::regtype::TEXT) = 'INTEGER', 'id column should be
INTEGER type')
- FROM pg_attribute WHERE attrelid = 'cifar10_predict'::regclass
- AND attname = 'id';
+SELECT assert(UPPER(pg_typeof(id)::TEXT )= 'INTEGER',
+ 'id column should be INTEGER type') FROM cifar10_predict;
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
'SMALLINT', 'prediction column should be SMALLINT type')
- FROM pg_attribute WHERE attrelid = 'cifar10_predict'::regclass
- AND attname = 'estimated_y';
+FROM cifar10_predict;
-- Validate correct number of rows returned.
-SELECT assert(COUNT(*)=2, 'Output table of madlib_keras_predict should have
two rows') FROM cifar10_predict;
+SELECT assert(COUNT(*)=2, 'Output table of madlib_keras_predict should have
two rows')
+FROM cifar10_predict;
-- First test that all values are in set of class values; if this breaks, it's
definitely a problem.
SELECT assert(estimated_y IN (0,1),
- 'Predicted value not in set of defined class values for model')
+ 'Predicted value not in set of defined class values for model')
FROM cifar10_predict;
DROP TABLE IF EXISTS cifar10_predict;
@@ -512,15 +511,13 @@ SELECT madlib_keras_predict(
'prob',
0);
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof(prob_0)::TEXT) =
'DOUBLE PRECISION', 'column prob_0 should be double precision type')
- FROM pg_attribute WHERE attrelid = 'cifar10_predict'::regclass
- AND attname = 'prob_0';
+FROM cifar10_predict;
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof(prob_1)::TEXT) =
'DOUBLE PRECISION', 'column prob_1 should be double precision type')
- FROM pg_attribute WHERE attrelid = 'cifar10_predict'::regclass
- AND attname = 'prob_1';
+FROM cifar10_predict;
SELECT assert(COUNT(*)=3, 'Predict out table must have exactly three cols.')
FROM pg_attribute
@@ -616,20 +613,17 @@ SELECT madlib_keras_predict(
-- Validate the output datatype of newly created prediction columns
-- for prediction type = 'prob' and class_values 'TEXT' with NULL as a valid
-- class_values
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof(prob_cat)::TEXT) =
'DOUBLE PRECISION', 'column prob_cat should be double precision type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass AND attname = 'prob_cat';
+FROM cifar10_predict;
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof(prob_dog)::TEXT) =
'DOUBLE PRECISION', 'column prob_dog should be double precision type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass AND attname = 'prob_dog';
+FROM cifar10_predict;
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass AND attname = 'prob_NULL';
+FROM cifar10_predict;
-- Must have exactly 4 cols (3 for class_values and 1 for id)
SELECT assert(COUNT(*)=4, 'Predict out table must have exactly four cols.')
@@ -650,11 +644,10 @@ SELECT madlib_keras_predict(
-- Validate the output datatype of newly created prediction columns
-- for prediction type = 'response' and class_values 'TEXT' with NULL
-- as a valid class_values
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
- 'TEXT', 'prediction column should be TEXT type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass
- AND attname = 'estimated_y';
+SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) = 'TEXT',
+ 'prediction column should be TEXT type')
+FROM cifar10_predict LIMIT 1;
+
-- Tests where the assumption is user has one-hot encoded, so class_values
-- in input summary table will be NULL.
@@ -674,10 +667,9 @@ SELECT madlib_keras_predict(
-- Validate the output datatype of newly created prediction column
-- for prediction type = 'response' and class_value = NULL
-- Returns: Array of probabilities for user's one-hot encoded data
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
- 'DOUBLE PRECISION[]', 'column prob should be double precision[] type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass AND attname = 'prob';
+SELECT assert(UPPER(pg_typeof(prob)::TEXT) = 'DOUBLE PRECISION[]',
+ 'column prob should be double precision[] type')
+FROM cifar10_predict LIMIT 1;
-- Predict with pred_type=response
DROP TABLE IF EXISTS cifar10_predict;
@@ -694,11 +686,14 @@ SELECT madlib_keras_predict(
-- for prediction type = 'response' and class_value = NULL
-- Returns: Index of class value in user's one-hot encoded data with
-- highest probability
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
- 'DOUBLE PRECISION', 'prediction column should be double precision type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass
- AND attname = 'estimated_y';
+SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) = 'TEXT',
+ 'column estimated_y_text should be text type')
+FROM cifar10_predict LIMIT 1;
+
+SELECT assert(
+ estimated_y_text IN ('0', '1'),
+ 'Predict failure for null class value and response pred_type.')
+FROM cifar10_predict;
-- Test predict with INTEGER class_values
-- with NULL as a valid class value
@@ -747,13 +742,11 @@ SELECT madlib_keras_predict(
-- Validate the output datatype of newly created prediction column
-- for prediction type = 'prob' and class_values 'INT' with NULL
-- as a valid class_values
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass AND attname = 'prob_NULL';
-
+FROM cifar10_predict;
-- Must have exactly 6 cols (5 for class_values and 1 for id)
-SELECT assert(COUNT(*)=6, 'Predict out table must have exactly four cols.')
+SELECT assert(COUNT(*)=6, 'Predict out table must have exactly six cols.')
FROM pg_attribute
WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
@@ -772,10 +765,9 @@ SELECT madlib_keras_predict(
-- for prediction type = 'response' and class_values 'TEXT' with NULL
-- as a valid class_values
-- Returns: class_value with highest probability
-SELECT assert(UPPER(atttypid::regtype::TEXT) =
+SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
'SMALLINT', 'prediction column should be smallint type')
-FROM pg_attribute
-WHERE attrelid = 'cifar10_predict'::regclass AND attname = 'estimated_y';
+FROM cifar10_predict;
-- Test case with a different input shape (3, 32, 32) instead of (32, 32, 3).
-- Create a new table with image shape 3, 32, 32
@@ -1066,6 +1058,27 @@ SELECT madlib_keras_fit('iris_data_packed', -- source
table
1 -- metrics_compute_frequency
);
+DROP TABLE IF EXISTS iris_train, iris_test;
+-- Set seed so results are reproducible
+SELECT setseed(0);
+SELECT train_test_split('iris_data', -- Source table
+ 'iris', -- Output table root name
+ 0.8, -- Train proportion
+ NULL, -- Test proportion (0.2)
+ NULL, -- Strata definition
+ NULL, -- Output all columns
+ NULL, -- Sample without replacement
+ TRUE -- Separate output tables
+ );
+
+DROP TABLE IF EXISTS iris_predict;
+SELECT madlib_keras_predict('iris_model', -- model
+ 'iris_test', -- test_table
+ 'id', -- id column
+ 'attributes', -- independent var
+ 'iris_predict' -- output table
+ );
+
-- Test that our code is indeed learning something and not broken. The loss
-- from the first iteration should be less than the 5th, while the accuracy
-- must be greater.
@@ -1179,3 +1192,96 @@ SELECT assert(
abs(first.training_metrics_final-second.training_metrics[2]) < 1e-10,
'Transfer learning test failed because training loss and metrics don''t
match the expected value.')
FROM iris_model_first_run AS first, iris_model_transfer_summary AS second;
+
+---------------------- Predict BYOM test --------------------------------
+
+-- class_values not NULL, pred_type is response
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom',
+ 'response',
+ -1,
+ ARRAY['Iris-setosa', 'Iris-versicolor',
+ 'Iris-virginica']
+ );
+
+SELECT assert(
+ p0.estimated_class_text = p1.estimated_dependent_var,
+ 'Predict byom failure for non null class value and response pred_type.')
+FROM iris_predict AS p0, iris_predict_byom AS p1
+WHERE p0.id=p1.id;
+SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
+ 'Predict byom failure for non null class value and response pred_type.
+ Expeceted estimated_dependent_var to be of type TEXT')
+FROM iris_predict_byom LIMIT 1;
+
+-- class_values NULL, pred_type is NULL (response)
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom'
+ );
+SELECT assert(
+ p1.estimated_dependent_var IN ('0', '1', '2'),
+ 'Predict byom failure for null class value and null pred_type.')
+FROM iris_predict_byom AS p1;
+SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
+ 'Predict byom failure for non null class value and response pred_type.
+ Expeceted estimated_dependent_var to be of type TEXT')
+FROM iris_predict_byom LIMIT 1;
+
+-- class_values not NULL, pred_type is prob
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom',
+ 'prob',
+ -1,
+ ARRAY['Iris-setosa', 'Iris-versicolor',
+ 'Iris-virginica'],
+ 1.0
+ );
+
+SELECT assert(
+ (p1."prob_Iris-setosa" + p1."prob_Iris-virginica" +
p1."prob_Iris-versicolor") - 1 < 1e-6,
+ 'Predict byom failure for non null class value and prob pred_type.')
+FROM iris_predict_byom AS p1;
+SELECT assert(UPPER(pg_typeof("prob_Iris-setosa")::TEXT) = 'DOUBLE PRECISION',
+ 'Predict byom failure for non null class value and prob pred_type.
+ Expeceted "prob_Iris-setosa" to be of type DOUBLE PRECISION')
+FROM iris_predict_byom LIMIT 1;
+
+-- class_values NULL, pred_type is prob
+DROP TABLE IF EXISTS iris_predict_byom;
+SELECT madlib_keras_predict_byom(
+ 'iris_model_arch',
+ 2,
+ 'iris_test',
+ 'id',
+ 'attributes',
+ 'iris_predict_byom',
+ 'prob',
+ 0,
+ NULL
+ );
+SELECT assert(
+ (prob[1] + prob[2] + prob[3]) - 1 < 1e-6,
+ 'Predict byom failure for null class value and prob pred_type.')
+FROM iris_predict_byom;
+SELECT assert(UPPER(pg_typeof(prob)::TEXT) = 'DOUBLE PRECISION[]',
+ 'Predict byom failure for null class value and prob pred_type.
Expeceted prob to
+ be of type DOUBLE PRECISION[]')
+FROM iris_predict_byom LIMIT 1;
diff --git
a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
index 2a1c39e..9cce86a 100644
---
a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
+++
b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
@@ -301,7 +301,7 @@ class MadlibKerasFitTestCase(unittest.TestCase):
self.assertEqual(True, res)
-class MadlibKerasPredictTestCase(unittest.TestCase):
+class InternalKerasPredictTestCase(unittest.TestCase):
def setUp(self):
self.plpy_mock = Mock(spec='error')
patches = {
@@ -406,6 +406,90 @@ class MadlibKerasPredictTestCase(unittest.TestCase):
self.assertEqual(False, 'row_count' in k['SD'])
self.assertEqual(False, 'segment_model_predict' in k['SD'])
+
+class MadlibKerasPredictBYOMTestCase(unittest.TestCase):
+ def setUp(self):
+ self.plpy_mock = Mock(spec='error')
+ patches = {
+ 'plpy': plpy
+ }
+
+ self.plpy_mock_execute = MagicMock()
+ plpy.execute = self.plpy_mock_execute
+
+ self.module_patcher = patch.dict('sys.modules', patches)
+ self.module_patcher.start()
+ self.num_classes = 5
+ self.model = Sequential()
+ self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
+ input_shape=(1,1,1,), padding='same'))
+ self.model.add(Dense(self.num_classes))
+
+ self.pred_type = 'prob'
+ self.gpus_per_host = 2
+ self.class_values = ['foo', 'bar', 'baaz', 'foo2', 'bar2']
+ self.normalizing_const = 255.0
+
+ import madlib_keras_predict
+ self.module = madlib_keras_predict
+ self.module.get_model_arch_weights = Mock(return_value=(
+ self.model.to_json(), 'weights'))
+ self.module.InputValidator.validate_predict_byom_tables = Mock()
+ self.module.InputValidator.validate_input_shape = Mock()
+ self.module.BasePredict.call_internal_keras = Mock()
+
+ def tearDown(self):
+ self.module_patcher.stop()
+
+ def test_predictbyom_defaults_1(self):
+ res = self.module.PredictBYOM('schema_madlib', 'model_arch_table',
+ 'model_arch_id', 'test_table', 'id_col',
+ 'independent_varname', 'output_table', None,
+ None, None, None)
+ self.assertEqual('response', res.pred_type)
+ self.assertEqual(0, res.gpus_per_host)
+ self.assertEqual([0,1,2,3,4], res.class_values)
+ self.assertEqual(1.0, res.normalizing_const)
+ self.assertEqual('text', res.dependent_vartype)
+
+ def test_predictbyom_defaults_2(self):
+ res = self.module.PredictBYOM('schema_madlib', 'model_arch_table',
+ 'model_arch_id', 'test_table', 'id_col',
+ 'independent_varname', 'output_table',
+ self.pred_type, self.gpus_per_host,
+ self.class_values,
self.normalizing_const)
+ self.assertEqual('prob', res.pred_type)
+ self.assertEqual(2, res.gpus_per_host)
+ self.assertEqual(['foo', 'bar', 'baaz', 'foo2', 'bar2'],
res.class_values)
+ self.assertEqual(255.0, res.normalizing_const)
+ self.assertEqual('double precision', res.dependent_vartype)
+
+ def test_predictbyom_exception_invalid_params(self):
+ with self.assertRaises(plpy.PLPYException) as error:
+ self.module.PredictBYOM('schema_madlib', 'model_arch_table',
+ 'model_arch_id', 'test_table', 'id_col',
+ 'independent_varname', 'output_table',
+ 'invalid_pred_type', self.gpus_per_host,
+ self.class_values, self.normalizing_const)
+ self.assertIn('invalid_pred_type', str(error.exception))
+
+ with self.assertRaises(plpy.PLPYException) as error:
+ self.module.PredictBYOM('schema_madlib', 'model_arch_table',
+ 'model_arch_id', 'test_table', 'id_col',
+ 'independent_varname', 'output_table',
+ self.pred_type, self.gpus_per_host,
+ ["foo", "bar", "baaz"],
self.normalizing_const)
+ self.assertIn('class values', str(error.exception).lower())
+
+ with self.assertRaises(plpy.PLPYException) as error:
+ self.module.PredictBYOM('schema_madlib', 'model_arch_table',
+ 'model_arch_id', 'test_table', 'id_col',
+ 'independent_varname', 'output_table',
+ self.pred_type, self.gpus_per_host,
+ self.class_values, 0)
+ self.assertIn('normalizing const', str(error.exception).lower())
+
+
class MadlibKerasWrapperTestCase(unittest.TestCase):
def setUp(self):
self.plpy_mock = Mock(spec='error')
@@ -748,58 +832,37 @@ class
MadlibKerasFitInputValidatorTestCase(unittest.TestCase):
def tearDown(self):
self.module_patcher.stop()
- def test_validate_input_shapes_shapes_do_not_match(self):
- self.plpy_mock_execute.return_value = [{'n_0': 32, 'n_1': 32}]
- self.subject._validate_input_args = Mock()
- with self.assertRaises(plpy.PLPYException):
- self.subject._validate_input_shapes(
- 'dummy_tbl', 'dummy_col', [32,32,3], 2)
-
- self.plpy_mock_execute.return_value = [{'n_0': 3, 'n_1': 32, 'n_2':
32}]
- with self.assertRaises(plpy.PLPYException):
- self.subject._validate_input_shapes(
- 'dummy_tbl', 'dummy_col', [32,32,3], 2)
-
- self.plpy_mock_execute.return_value = [{'n_0': 3, 'n_1': None, 'n_2':
None}]
- with self.assertRaises(plpy.PLPYException):
- self.subject._validate_input_shapes(
- 'dummy_tbl', 'dummy_col', [3,32], 2)
-
- def test_validate_input_shapes_shapes_match(self):
- self.plpy_mock_execute.return_value = [{'n_0': 32, 'n_1': 32, 'n_2':
3}]
- self.subject._validate_input_args = Mock()
- self.subject._validate_input_shapes(
- 'dummy_tbl', 'dummy_col', [32,32,3], 1)
def test_is_valid_metrics_compute_frequency_True_None(self):
self.subject.FitInputValidator._validate_input_args = Mock()
obj = self.subject.FitInputValidator(
- 'test_table', 'val_table', 'model_table', 'model_arch_table',
+ 'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
'dep_varname', 'independent_varname', 5, None, False)
self.assertEqual(True, obj._is_valid_metrics_compute_frequency())
def test_is_valid_metrics_compute_frequency_True_num(self):
self.subject.FitInputValidator._validate_input_args = Mock()
obj = self.subject.FitInputValidator(
- 'test_table', 'val_table', 'model_table', 'model_arch_table',
+ 'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
'dep_varname', 'independent_varname', 5, 3, False)
self.assertEqual(True, obj._is_valid_metrics_compute_frequency())
def test_is_valid_metrics_compute_frequency_False_zero(self):
self.subject.FitInputValidator._validate_input_args = Mock()
obj = self.subject.FitInputValidator(
- 'test_table', 'val_table', 'model_table', 'model_arch_table',
+ 'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
'dep_varname', 'independent_varname', 5, 0, False)
self.assertEqual(False, obj._is_valid_metrics_compute_frequency())
def test_is_valid_metrics_compute_frequency_False_greater(self):
self.subject.FitInputValidator._validate_input_args = Mock()
obj = self.subject.FitInputValidator(
- 'test_table', 'val_table', 'model_table', 'model_arch_table',
+ 'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
'dep_varname', 'independent_varname', 5, 6, False)
self.assertEqual(False, obj._is_valid_metrics_compute_frequency())
-class PredictInputValidatorTestCases(unittest.TestCase):
+
+class InputValidatorTestCase(unittest.TestCase):
def setUp(self):
self.plpy_mock = Mock(spec='error')
patches = {
@@ -813,34 +876,83 @@ class PredictInputValidatorTestCases(unittest.TestCase):
self.module_patcher.start()
import madlib_keras_validator
self.module = madlib_keras_validator
- self.module.PredictInputValidator._validate_input_args = Mock()
- self.subject = self.module.PredictInputValidator(
- 'test_table', 'model_table', 'id_col', 'independent_varname',
- 'output_table', 'pred_type', 'module_name')
+ self.subject = self.module.InputValidator
+
+ self.module_name = 'module'
+ self.test_table = 'test_table'
+ self.model_table = 'model_table'
+ self.id_col = 'id_col'
+ self.ind_var = 'ind_var'
+ self.model_arch_table = 'model_arch_table'
+ self.model_arch_id = 2
+ self.num_classes = 1598
+ self.model = Sequential()
+ self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
+ input_shape=(1,1,1,), padding='same'))
+ self.model.add(Dense(self.num_classes))
self.classes = ['train', 'boat', 'car', 'airplane']
def tearDown(self):
self.module_patcher.stop()
def test_validate_pred_type_invalid_pred_type(self):
- self.subject.pred_type = 'invalid'
+ with self.assertRaises(plpy.PLPYException) as error:
+ self.subject.validate_pred_type(
+ self.module_name, 'invalid_pred_type', ['cat', 'dog'])
+ self.assertIn('type', str(error.exception).lower())
+
+ def test_validate_class_values_greater_than_1600_class_values(self):
+ self.model.add(Dense(1599))
+ with self.assertRaises(plpy.PLPYException) as error:
+ self.subject.validate_class_values(
+ self.module_name, range(1599), 'prob', self.model.to_json())
+ self.assertIn('1600', str(error.exception))
+
+ def test_validate_class_values_valid_class_values_prob(self):
+ self.subject.validate_class_values(
+ self.module_name, range(self.num_classes), 'prob',
self.model.to_json())
+ self.subject.validate_class_values(
+ self.module_name, None, 'prob', self.model.to_json())
+
+ def
test_validate_class_values_valid_pred_type_valid_class_values_response(self):
+ self.subject.validate_class_values(
+ self.module_name, range(self.num_classes), 'response',
self.model.to_json())
+ self.subject.validate_class_values(
+ self.module_name, None, 'response', self.model.to_json())
+
+ def test_validate_input_shape_shapes_do_not_match(self):
+ self.plpy_mock_execute.return_value = [{'n_0': 32, 'n_1': 32}]
+ with self.assertRaises(plpy.PLPYException):
+ self.subject.validate_input_shape(
+ self.test_table, self.ind_var, [32,32,3], 2)
+
+ self.plpy_mock_execute.return_value = [{'n_0': 3, 'n_1': 32, 'n_2':
32}]
with self.assertRaises(plpy.PLPYException):
- self.subject.validate_pred_type(['cat', 'dog'])
+ self.subject.validate_input_shape(
+ self.test_table, self.ind_var, [32,32,3], 2)
- def test_validate_pred_type_valid_pred_type_invalid_num_class_values(self):
- self.subject.pred_type = 'prob'
+ self.plpy_mock_execute.return_value = [{'n_0': 3, 'n_1': None, 'n_2':
None}]
with self.assertRaises(plpy.PLPYException):
- self.subject.validate_pred_type(range(1599))
+ self.subject.validate_input_shape(
+ self.test_table, self.ind_var, [3,32], 2)
- def test_validate_pred_type_valid_pred_type_valid_class_values_prob(self):
- self.subject.pred_type = 'prob'
- self.subject.validate_pred_type(range(1598))
- self.subject.validate_pred_type(None)
+ def test_validate_input_shape_shapes_match(self):
+ self.plpy_mock_execute.return_value = [{'n_0': 32, 'n_1': 32, 'n_2':
3}]
+ self.subject.validate_input_shape(
+ self.test_table, self.ind_var, [32,32,3], 1)
+
+ def test_validate_model_arch_table_none_values(self):
+ with self.assertRaises(plpy.PLPYException) as error:
+ obj = self.subject.validate_model_arch_table(
+ self.module_name, None, self.model_arch_id)
+ self.assertIn('null', str(error.exception).lower())
+
+ self.module.input_tbl_valid = Mock()
+ with self.assertRaises(plpy.PLPYException) as error:
+ obj = self.subject.validate_model_arch_table(
+ self.module_name, self.model_arch_table, None)
+ self.assertIn('id', str(error.exception).lower())
- def
test_validate_pred_type_valid_pred_type_valid_class_values_response(self):
- self.subject.pred_type = 'response'
- self.subject.validate_pred_type(range(1598))
- self.subject.validate_pred_type(None)
class MadlibSerializerTestCase(unittest.TestCase):
def setUp(self):
@@ -921,6 +1033,7 @@ class MadlibSerializerTestCase(unittest.TestCase):
self.assertEqual(np.array([0,1,3,4,5], dtype=np.float32).tostring(),
res)
+
class MadlibKerasHelperTestCase(unittest.TestCase):
def setUp(self):
self.plpy_mock = Mock(spec='error')
