Github user edespino commented on a diff in the pull request:
https://github.com/apache/incubator-madlib/pull/149#discussion_r127095011
--- Diff: src/ports/postgres/modules/convex/mlp_igd.py_in ---
@@ -0,0 +1,754 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+@file mlp_igd.py_in
+
+@brief Multilayer perceptron using IGD: Driver functions
+
+@namespace mlp_igd
+"""
+import plpy
+
+from utilities.control import MinWarning
+from utilities.utilities import add_postfix
+from utilities.utilities import py_list_to_sql_string
+from utilities.utilities import extract_keyvalue_params
+from utilities.utilities import _assert
+from utilities.utilities import unique_string
+from utilities.utilities import strip_end_quotes
+
+from utilities.validate_args import cols_in_tbl_valid
+from utilities.validate_args import input_tbl_valid
+from utilities.validate_args import is_var_valid
+from utilities.validate_args import output_tbl_valid
+from utilities.validate_args import get_expr_type
+from utilities.validate_args import array_col_has_same_dimension
+from utilities.validate_args import array_col_dimension
+
+
+def mlp(schema_madlib, source_table, output_table, independent_varname,
+ dependent_varname, hidden_layer_sizes,
+ optimizer_param_str, activation, is_classification, **kwargs):
+ """
+ Args:
+ @param schema_madlib
+ @param source_table
+ @param output_table
+ @param independent_varname
+ @param dependent_varname
+ @param hidden_layer_sizes
+ @param optimizer_param_str
+
+ Returns:
+ None
+ """
+ with MinWarning('info'):
+ optimizer_params = _get_optimizer_params(optimizer_param_str or "")
+ _validate_args(source_table, output_table, independent_varname,
+ dependent_varname, hidden_layer_sizes,
+ optimizer_params, is_classification)
+
+ current_iteration = 1
+ prev_state = None
+ tolerance = optimizer_params["tolerance"]
+ n_iterations = optimizer_params["n_iterations"]
+ step_size = optimizer_params["step_size"]
+ n_tries = optimizer_params["n_tries"]
+ activation_name = _get_activation_function_name(activation)
+ activation_index = _get_activation_index(activation_name)
+ num_input_nodes = array_col_dimension(source_table,
independent_varname)
+ num_output_nodes = 0
+ classes = []
+ dependent_type = get_expr_type(dependent_varname, source_table)
+ original_dependent_varname = dependent_varname
+
+ if(is_classification):
+ dependent_variable_sql = """
+ SELECT DISTINCT {dependent_varname}
+ FROM {source_table}
+ """.format(dependent_varname=dependent_varname,
+ source_table=source_table)
+ labels = plpy.execute(dependent_variable_sql)
+ one_hot_dependent_varname = 'ARRAY['
+ num_output_nodes = len(labels)
+ for x in labels:
+ label = _format_label(x[dependent_varname])
+ classes.append(label)
+ one_hot_dependent_varname += dependent_varname + "=" +
str(label) + ","
+ # Remove the last comma
+ one_hot_dependent_varname = one_hot_dependent_varname[:-1]
+ one_hot_dependent_varname += ']::integer[]'
+ dependent_varname = one_hot_dependent_varname
+ if(not is_classification):
+ if "[]" not in dependent_type:
+ dependent_varname = "ARRAY[" + dependent_varname + "]"
+ num_output_nodes = array_col_dimension(source_table,
dependent_varname)
+ layer_sizes = [num_input_nodes] + hidden_layer_sizes +
[num_output_nodes]
+
+ while True:
+ if prev_state:
+ prev_state_str = py_list_to_sql_string(prev_state,
array_type="double precision")
+ else:
+ prev_state_str = "(NULL)::DOUBLE PRECISION[]"
+ train_sql = """
+ SELECT
+ {schema_madlib}.mlp_igd_step(
+ ({independent_varname})::DOUBLE PRECISION[],
+ ({dependent_varname})::DOUBLE PRECISION[],
+ {prev_state},
+ {layer_sizes},
+ ({step_size})::FLOAT8,
+ {activation},
+ {is_classification}) as curr_state
+ FROM {source_table} AS _src
+ """.format(schema_madlib=schema_madlib,
+ independent_varname=independent_varname,
+ dependent_varname=dependent_varname,
+ prev_state=prev_state_str,
+ # C++ uses double internally
+ layer_sizes=py_list_to_sql_string(layer_sizes,
+
array_type="double precision"),
+ step_size=step_size,
+ source_table=source_table,
+ activation=activation_index,
+ is_classification=int(is_classification))
+ curr_state = plpy.execute(train_sql)[0]["curr_state"]
+ dist_sql = """
+ SELECT {schema_madlib}.internal_mlp_igd_distance(
+ {prev_state},
+ {curr_state}) as state_dist
+ """.format(schema_madlib=schema_madlib,
+ prev_state=prev_state_str,
+ curr_state=py_list_to_sql_string(curr_state,
"double precision"),
+ tolerance=tolerance)
+ state_dist = plpy.execute(dist_sql)[0]["state_dist"]
+ if ((state_dist and state_dist < tolerance) or
+ current_iteration > n_iterations):
+ break
+ prev_state = curr_state
+ # plpy.info("Iteration:
"+str(current_iteration)+"/"+str(n_iterations))
+ current_iteration += 1
+ _build_model_table(schema_madlib, output_table, curr_state,
n_iterations)
+ layer_sizes_str=py_list_to_sql_string(layer_sizes,
array_type="integer")
+ classes_str=py_list_to_sql_string([strip_end_quotes(cl, "'") for
cl in classes],
+ array_type=dependent_type)
+ # TODO validation for summary
+ summary_table_creation_query = """
+ CREATE TABLE {output_table}_summary(
+ source_table TEXT,
+ independent_varname TEXT,
+ dependent_varname TEXT,
+ tolerance FLOAT,
+ step_size FLOAT,
+ n_iterations INTEGER,
+ n_tries INTEGER,
+ layer_sizes INTEGER[],
+ activation_function TEXT,
+ is_classification BOOLEAN,
+ classes {dependent_type}[]
+ )""".format(output_table=output_table,
+ dependent_type=dependent_type)
+
+ summary_table_update_query = """
+ INSERT INTO {output_table}_summary VALUES(
+ '{source_table}',
+ '{independent_varname}',
+ '{original_dependent_varname}',
+ {tolerance},
+ {step_size},
+ {n_iterations},
+ {n_tries},
+ {layer_sizes_str},
+ '{activation_name}',
+ {is_classification},
+ {classes_str}
+ )
+ """.format(**locals())
+ plpy.execute(summary_table_creation_query)
+ plpy.execute(summary_table_update_query)
+# ----------------------------------------------------------------------
+
+
+def _build_model_table(schema_madlib, output_table, final_state,
n_iterations):
+ final_state_str = py_list_to_sql_string(final_state,
array_type="double precision")
+
+ model_table_query = """
+ CREATE TABLE {output_table} AS
+ SELECT
+ (result).coeff AS coeff,
+ (result).loss AS loss,
+ {n_iterations} AS num_iterations
+ -- (result).num_rows_processed AS num_rows_processed,
+ -- n_tuples_including_nulls - (result).num_rows_processed
+ FROM (
+ SELECT
+ {schema_madlib}.internal_mlp_igd_result(
+ {final_state_str}
+ ) AS result
+ ) rel_state_subq
+ """.format(**locals())
+ plpy.execute(model_table_query)
+# ----------------------------------------------------------------------
+
+
+def _get_optimizer_params(param_str):
+ params_defaults = {
+ "step_size": (0.001, float),
+ "n_iterations": (100, int),
+ "n_tries": (1, int),
+ "tolerance": (0.001, float),
+ }
+ param_defaults = dict([(k, v[0]) for k, v in params_defaults.items()])
+ param_types = dict([(k, v[1]) for k, v in params_defaults.items()])
+
+ if not param_str:
+ return param_defaults
+
+ name_value = extract_keyvalue_params(param_str, param_types,
param_defaults,
+ ignore_invalid=True)
+ return name_value
+# ----------------------------------------------------------------------
+
+
+def _validate_args_classification(source_table, output_table,
independent_varname,
+ dependent_varname, hidden_layer_sizes,
+ optimizer_params):
+ expr_type = get_expr_type(dependent_varname, source_table)
+ int_types = ['integer', 'smallint', 'bigint']
+ text_types = ['text', 'varchar', 'character varying', 'char',
'character']
+ boolean_types = ['boolean']
+ _assert("[]" in expr_type or expr_type in int_types + text_types +
boolean_types,
+ "Dependent variable column should refer to an "
+ "integer, boolean, text, varchar, or character type.")
+# ----------------------------------------------------------------------
+
+
+def _validate_args_regression(source_table, output_table,
independent_varname,
+ dependent_varname, hidden_layer_sizes,
+ optimizer_params):
+ expr_type = get_expr_type(dependent_varname, source_table)
+ int_types = ['integer', 'smallint', 'bigint']
+ float_types = ['double precision', 'real']
+ _assert("[]" in expr_type or expr_type in int_types + float_types,
+ "Dependent variable column should refer to an array or numeric
type")
+ if("[]" in expr_type):
+ _assert(array_col_has_same_dimension(source_table,
dependent_varname),
+ "Dependent variable column should refer to arrays of the
same length")
+# ----------------------------------------------------------------------
+
+
+def _validate_args(source_table, output_table, independent_varname,
+ dependent_varname, hidden_layer_sizes,
+ optimizer_params, is_classification):
+ """
+ Args:
+ @param kwargs
+
+ Returns:
+
+ """
+ input_tbl_valid(source_table, "MLP")
+ output_tbl_valid(output_table, "MLP")
+ output_tbl_valid(output_table+"_summary", "MLP")
+ _assert(is_var_valid(source_table, independent_varname),
+ "MLP error: invalid independent_varname "
+ "('{independent_varname}') for source_table "
+
"({source_table})!".format(independent_varname=independent_varname,
+ source_table=source_table))
+
+ _assert(is_var_valid(source_table, dependent_varname),
+ "MLP error: invalid dependent_varname "
+ "('{dependent_varname}') for source_table "
+ "({source_table})!".format(dependent_varname=dependent_varname,
+ source_table=source_table))
+ _assert(hidden_layer_sizes is not None,
+ "hidden_layer_sizes may not be null")
+ _assert(type(hidden_layer_sizes) is list,
+ "hidden_layer_sizes must be an array of integers")
+ _assert(all(type(value) is int for value in hidden_layer_sizes),
+ "MLP error: Hidden layers sizes must be integers")
+ _assert(all(value >= 0 for value in hidden_layer_sizes),
+ "MLP error: Hidden layers sizes must be greater than 0.")
+ _assert(optimizer_params["tolerance"] >= 0,
+ "MLP error: Tolerance should be greater than or equal to 0.")
+ _assert(optimizer_params["n_tries"] >= 1,
+ "MLP error: Number of tries should be greater than or equal to
1")
+ _assert(optimizer_params["n_iterations"] >= 1,
+ "MLP error: Number of iterations should be greater than or
equal to 1")
+ _assert(optimizer_params["step_size"] > 0,
+ "MLP error: Stepsize should be greater than 0.")
+ _assert("[]" in get_expr_type(independent_varname, source_table),
+ "Independent variable column should refer to an array")
+ _assert(array_col_has_same_dimension(source_table,
independent_varname),
+ "Independent variable column should refer to arrays of the
same length")
+
+ if is_classification:
+ _validate_args_classification(source_table, output_table,
independent_varname,
+ dependent_varname,
hidden_layer_sizes,
+ optimizer_params)
+ else:
+ _validate_args_regression(source_table, output_table,
independent_varname,
+ dependent_varname, hidden_layer_sizes,
+ optimizer_params)
+# ----------------------------------------------------------------------
+
+
+def _get_activation_function_name(activation_function):
+ if not activation_function:
+ activation_function = 'sigmoid'
+ else:
+ # Add non-linear kernels below after implementing them.
+ supported_activation_function = ['sigmoid', 'tanh', 'relu']
+ try:
+ # allow user to specify a prefix substring of
+ # supported kernels. This works because the supported
+ # kernels have unique prefixes.
+ activation_function = next(x for x in
supported_activation_function
+ if
x.startswith(activation_function))
+ except StopIteration:
+ # next() returns a StopIteration if no element found
+ plpy.error("MLP Error: Invalid activation function: "
+ "{0}. Supported activation functions are ({1})"
+ .format(activation_function,
','.join(sorted(supported_activation_function))))
+ return activation_function
+#
------------------------------------------------------------------------------
+
+
+def _get_activation_index(activation_name):
+ table = {"relu": 0, "sigmoid": 1, "tanh": 2}
+ return table[activation_name]
+
+
+def _format_label(label):
+ if type(label) is str:
+ return "'" + label + "'"
+ return label
+# -------------------------------------------------------------------------
+
+
+def mlp_predict(schema_madlib, model_table, data_table,
+ id_col_name, output_table,
+ pred_type='response', **kwargs):
+ """ Score new observations using a trained neural network
+
+ @param schema_madlib Name of the schema where MADlib is installed
+ @param model_table Name of learned model
+ @param data_table Name of table/view containing the data
+ points to be scored
+ @param id_col_name Name of column in source_table containing
+ (integer) identifier for data point
+ @param output_table Name of table to store the results
+ @param pred_type: str, The type of output required:
+ 'response' gives the actual response values,
+ 'prob' gives the probability of the classes in a
+ For regression, only type='response' is defined.
+ """
+ # model table
+ input_tbl_valid(model_table, 'MLP')
+ cols_in_tbl_valid(model_table, ['coeff'], 'MLP')
+ # summary table
+ summary_table = add_postfix(model_table, "_summary")
+ input_tbl_valid(summary_table, 'MLP')
+ cols_in_tbl_valid(summary_table,
+ ['dependent_varname', 'independent_varname',
+ 'activation_function',
+ 'tolerance', 'step_size','n_iterations',
+ 'n_tries', 'classes', 'layer_sizes',
'source_table'],
+ 'MLP')
+
+ # read necessary info from summary
+ summary = plpy.execute("SELECT * FROM {0}".format(summary_table))[0]
+ coeff = py_list_to_sql_string(plpy.execute("SELECT * FROM
{0}".format(model_table))[0]["coeff"])
+ dependent_varname = summary['dependent_varname']
+ independent_varname = summary['independent_varname']
+ source_table = summary['source_table']
+ activation_function =
_get_activation_index(summary['activation_function'])
+ layer_sizes = py_list_to_sql_string(summary['layer_sizes'],
array_type="DOUBLE PRECISION")
+ is_classification = int(summary["is_classification"])
+ is_response = int(pred_type=='response')
+
+ pred_name = ('"prob_{0}"' if pred_type == "prob" else
+ '"estimated_{0}"').format(dependent_varname.replace('"',
'').strip())
+
+ input_tbl_valid(data_table, 'MLP')
+
+ _assert(is_var_valid(data_table, independent_varname),
+ "MLP Error: independent_varname ('{0}') is invalid for
data_table ({1})".
+ format(independent_varname, data_table))
+ _assert(id_col_name is not None, "MLP Error: id_col_name is NULL")
+ _assert(is_var_valid(data_table, id_col_name),
+ "MLP Error: id_col_name ('{0}') is invalid for {1}".
+ format(id_col_name, data_table))
+ output_tbl_valid(output_table, 'MLP')
+ # optimizer_param_dict = _get_optimizer_params(optimizer_params)
+
+ with MinWarning("warning"):
+ header = "CREATE TABLE " + output_table + " AS "
+ # Regression
+ if not is_classification:
+ dependent_type = get_expr_type(dependent_varname, source_table)
+ unnest_if_not_array = ""
+ # Return the same type as the user provided. Internally we
always use an array, but
+ # if they provided a scaler, unnest it for the user
+ if("[]" not in dependent_type):
+ unnest_if_not_array = "UNNEST"
+ sql = header + """
+ SELECT {id_col_name},
+
{unnest_if_not_array}({schema_madlib}.internal_predict_mlp(
+ {coeff},
+ {independent_varname}::DOUBLE PRECISION[],
+ {is_classification},
+ {activation_function},
+ {layer_sizes},
+ {is_response}
+ )) as {pred_name}
+ FROM {data_table}
+ """
+ else:
+ summary_query = """
+ SELECT classes FROM {0}_summary
+ """.format(model_table)
+ classes = plpy.execute(summary_query)[0]['classes']
+ if pred_type == "response":
+ # This join is to recover the class name from the summary
table, as prediction just returns an index
+ classes_with_index_table = unique_string()
+ classes_table = unique_string()
+ sql = header + """
+ SELECT
+ q.{id_col_name}
+ ,(ARRAY{classes})[pred_idx[1]+1] as {pred_name}
+ FROM (
+ SELECT
+ {id_col_name},
+ {schema_madlib}.internal_predict_mlp(
+ {coeff}::DOUBLE PRECISION[],
+ {independent_varname}::DOUBLE
PRECISION[],
+ {is_classification},
+ {activation_function},
+ {layer_sizes},
+ {is_response}
+ )
+ as pred_idx
+ FROM {data_table}
+ ) q
+ """
+ else:
+ # Incomplete
+ intermediate_col = unique_string()
+ score_format = ',\n'.join([
+ '{interim}[{j}] as "estimated_prob_{c}"'.
+ format(j=i + 1, c=c.strip(' "'),
interim=intermediate_col)
+ for i, c in enumerate(classes)])
+ sql = header + """
+ SELECT
+ {id_col_name},
+ {score_format}
+ FROM (
+ SELECT {id_col_name},
+
{schema_madlib}.internal_predict_mlp_output(
+ {coeff}::DOUBLE PRECISION[],
+ {independent_varname}::DOUBLE
PRECISION[],
+ {is_classification},
+ {activation_function},
+ {layer_sizes},
+ {is_response}
+ )::TEXT[]
+ AS {intermediate_col}
+ FROM {data_table}
+ ) q
+ """
+#TODO add join for validation
+# as s {join_str} {model} as m {using_str}
+ sql = sql.format(**locals())
+ with MinWarning('warning'):
+ plpy.execute(sql)
+
+ pass
+# ----------------------------------------------------------------------
+
+def mlp_help(schema_madlib, message, is_classification):
+ method = 'mlp_classification' if is_classification else
'mlp_regression'
+ # TODO
+ int_types = ['integer', 'smallint', 'bigint']
+ text_types = ['text', 'varchar', 'character varying', 'char',
'character']
+ boolean_types = ['boolean']
+ supported_types = " " * 33 + ", ".join(text_types) + "\n" + " " * 33 +
", ".join(int_types + boolean_types)
+ label_description_classification = "Name of a column which specifies
label.\n" + " " * 33 + "Supported types are:\n" + supported_types
+ label_description_regression = "Dependent variable. May be an array
for multiple\n" +\
+ " " * 33 + "regression or the name of a column which is any\n" + "
" * 33 + "numeric type for single regression"
+ label_description = label_description_classification if
is_classification else label_description_regression
+ args = dict(schema_madlib=schema_madlib, method=method,
label_description=label_description)
+
+ summary = """
+ ----------------------------------------------------------------
+ SUMMARY
+ ----------------------------------------------------------------
+ Multi Layer Perceptron (MLP) is a model for regression and
+ classification
+
+ Also called "vanilla neural networks", they consist of several
+ fully connected hidden layers with non-linear activation
+ functions.
+
+ For more details on function usage:
+ SELECT {schema_madlib}.{method}('usage')
+
+ For a small example on using the function:
+ SELECT {schema_madlib}.{method}('example')""".format(**args)
+
+ usage = """
+
---------------------------------------------------------------------------
+ USAGE
+
---------------------------------------------------------------------------
+ SELECT {schema_madlib}.{method}(
+ source_table, -- name of input table
+ output_table, -- name of output model table
+ independent_varname, -- name of independent variable
+ dependent_varname, -- {label_description}
+ hidden_layer_sizes, -- Array of integers indicating the
+ number of hidden units per layer.
+ Length equal to the number of hidden
layers.
+ optimizer_params, -- optional, default NULL
+ parameters for optimization in
+ a comma-separated string of key-value
pairs.
+
+ step_size DOUBLE PRECISION, -- Default: 0.001
+ Learning rate
+ n_iterations INTEGER, -- Default: 100
+ Number of iterations per try
+ n_tries INTEGER, -- Default: 1
+ Total number of training cycles,
+ with random initializations to
avoid
+ local minima.
+ tolerance DOUBLE PRECISION, -- Default: 0.001
+ If the distance in loss between
+ two iterations is less than the
+ tolerance training will stop,
even if
+ n_iterations has not been
reached
+
+ activation -- optional, default: 'sigmoid'.
+ supported activations: 'relu', 'sigmoid',
+ and 'tanh'
+ );
+
+
+
---------------------------------------------------------------------------
+ OUTPUT
+
---------------------------------------------------------------------------
+ The model table produced by MLP contains the following columns:
+
+ coeffs -- Flat array containing the weights of the neural
net
+
+ loss -- The total loss over the training data. Cross
entropy
+ for classification and MSE for regression
+
+ num_iterations -- The total number of training iterations
+
+ """.format(**args)
+
+ regression_example = """
+ - Create input table
+
+ CREATE TABLE lin_housing_wi (id serial, x float8[], grp_by_col int, y
float8);
+ COPY lin_housing_wi (x, grp_by_col, y) FROM STDIN NULL '?' DELIMITER
'|';
+
{1,0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98}
| 1 | 24.00
+
{1,0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14} |
1 | 21.60
+
{1,0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03} |
1 | 34.70
+
{1,0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94} |
1 | 33.40
+
{1,0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33} |
1 | 36.20
+
{1,0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21} |
1 | 28.70
+
{1,0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43}
| 1 | 22.90
+
{1,0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15}
| 1 | 27.10
+
{1,0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93}
| 1 | 16.50
+
{1,0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10}
| 1 | 18.90
+
{1,0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45}
| 1 | 15.00
+
{1,0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27}
| 1 | 18.90
+
{1,0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71}
| 1 | 21.70
+ \.
+
+ - Generate a multilayer perception with a two hidden layers of 5 units
+ each. Use the x column as the independent variables, and use the class
+ column as the classification. Set the tolerance to 0 so that 300
+ iterations will be run. Use a sigmoid activation function.
+ The model will be written to mlp_regress_result.
+
+ SELECT mlp_regression(
+ 'lin_housing_wi', -- Source table
+ 'mlp_regress_result', -- Desination table
+ 'x', -- Independent variable
+ 'y', -- Dependent variable
+ ARRAY[5,5], -- Number of hidden units per layer
+ 'step_size=0.007,
+ n_iterations=300,
+ tolerance=0',
+ 'sigmoid'); -- Activation
+
+ """
+
+ classification_example = """
+ -- Create input table
+
+ CREATE TABLE iris_data(
+ id integer,
+ attributes numeric[],
+ class_text varchar,
+ class integer
+ );
+
+ INSERT INTO iris_data VALUES
+ (1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa',1),
+ (2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa',1),
+ (3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa',1),
+ (4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa',1),
+ (5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa',1),
+ (6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa',1),
+ (7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa',1),
+ (8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa',1),
+ (9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa',1),
+ (10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa',1),
+ (11,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor',2),
+ (12,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor',2),
+ (13,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor',2),
+ (14,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor',2),
+ (15,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor',2),
+ (16,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor',2),
+ (17,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor',2),
+ (18,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor',2),
+ (19,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor',2),
+ (20,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor',2),
--- End diff --
In the INSERT example, the trailing comma (`,`) should be a semi-colon
(`;`).
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