Github user kaknikhil commented on a diff in the pull request:
https://github.com/apache/madlib/pull/243#discussion_r175891761
--- Diff: src/ports/postgres/modules/convex/mlp_igd.py_in ---
@@ -72,107 +73,127 @@ def mlp(schema_madlib, source_table, output_table,
independent_varname,
"""
warm_start = bool(warm_start)
optimizer_params = _get_optimizer_params(optimizer_param_str or "")
+
+ tolerance = optimizer_params["tolerance"]
+ n_iterations = optimizer_params["n_iterations"]
+ step_size_init = optimizer_params["learning_rate_init"]
+ iterations_per_step = optimizer_params["iterations_per_step"]
+ power = optimizer_params["power"]
+ gamma = optimizer_params["gamma"]
+ step_size = step_size_init
+ n_tries = optimizer_params["n_tries"]
+ # lambda is a reserved word in python
+ lmbda = optimizer_params["lambda"]
+ batch_size = optimizer_params['batch_size']
+ n_epochs = optimizer_params['n_epochs']
+
summary_table = add_postfix(output_table, "_summary")
standardization_table = add_postfix(output_table, "_standardization")
- weights = '1' if not weights or not weights.strip() else
weights.strip()
hidden_layer_sizes = hidden_layer_sizes or []
- grouping_col = grouping_col or ""
- activation = _get_activation_function_name(activation)
- learning_rate_policy = _get_learning_rate_policy_name(
- optimizer_params["learning_rate_policy"])
- activation_index = _get_activation_index(activation)
-
+ # Note that we don't support weights with mini batching yet, so
validate
+ # this based on is_minibatch_enabled.
+ weights = '1' if not weights or not weights.strip() else
weights.strip()
_validate_args(source_table, output_table, summary_table,
standardization_table, independent_varname,
dependent_varname, hidden_layer_sizes, optimizer_params,
- is_classification, weights, warm_start, activation,
- grouping_col)
+ warm_start, activation, grouping_col)
+ is_minibatch_enabled = check_if_minibatch_enabled(source_table,
independent_varname)
+ _validate_params_based_on_minibatch(source_table, independent_varname,
+ dependent_varname, weights,
+ is_classification,
+ is_minibatch_enabled)
+ activation = _get_activation_function_name(activation)
+ learning_rate_policy = _get_learning_rate_policy_name(
+ optimizer_params["learning_rate_policy"])
+ activation_index = _get_activation_index(activation)
reserved_cols = ['coeff', 'loss', 'n_iterations']
+ grouping_col = grouping_col or ""
grouping_str, grouping_col = get_grouping_col_str(schema_madlib, 'MLP',
reserved_cols,
source_table,
grouping_col)
- current_iteration = 1
- prev_state = None
- tolerance = optimizer_params["tolerance"]
- n_iterations = optimizer_params["n_iterations"]
- step_size_init = optimizer_params["learning_rate_init"]
- iterations_per_step = optimizer_params["iterations_per_step"]
- power = optimizer_params["power"]
- gamma = optimizer_params["gamma"]
- step_size = step_size_init
- n_tries = optimizer_params["n_tries"]
- # lambda is a reserved word in python
- lmbda = optimizer_params["lambda"]
- iterations_per_step = optimizer_params["iterations_per_step"]
- num_input_nodes = array_col_dimension(source_table,
- independent_varname)
- num_output_nodes = 0
+ dependent_varname_backup = dependent_varname
classes = []
- dependent_type = get_expr_type(dependent_varname, source_table)
- original_dependent_varname = dependent_varname
-
- x_mean_table = unique_string(desp='x_mean_table')
- dimension, n_tuples = _tbl_dimension_rownum(schema_madlib,
source_table,
- independent_varname)
-
- tbl_data_scaled = unique_string(desp="tbl_data_scaled")
- col_ind_var_norm_new = unique_string(desp="ind_var_norm")
- col_dep_var_norm_new = unique_string(desp="dep_var_norm")
- # Standardize the data, and create a standardized version of the
- # source_table in tbl_data_scaled. Use this standardized table for IGD.
- normalize_data(locals())
- 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 label_obj in labels:
- label = _format_label(label_obj[dependent_varname])
- classes.append(label)
- classes.sort()
- for c in classes:
- one_hot_dependent_varname += col_dep_var_norm_new + \
- "=" + str(c) + ","
- # 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 is_minibatch_enabled:
+ mlp_preprocessor = MLPPreProcessor(source_table)
+ PSTDICT = mlp_preprocessor.preprocessed_summary_dict
--- End diff --
consider changing the name of this variable
---
