Github user njayaram2 commented on a diff in the pull request:
https://github.com/apache/incubator-madlib/pull/162#discussion_r132059184
--- Diff: src/ports/postgres/modules/convex/mlp_igd.py_in ---
@@ -59,60 +63,115 @@ def mlp(schema_madlib, source_table, output_table,
independent_varname,
Returns:
None
"""
- with MinWarning('warning'):
- optimizer_params = _get_optimizer_params(optimizer_param_str or "")
- summary_table = add_postfix(output_table, "_summary")
- _validate_args(source_table, output_table, summary_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 label_obj in labels:
- label = _format_label(label_obj[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
- else:
- 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]
+ warm_start = bool(warm_start)
+ optimizer_params = _get_optimizer_params(optimizer_param_str or "")
+ summary_table = add_postfix(output_table, "_summary")
+ weights = '1' if not weights or not weights.strip() else
weights.strip()
+ hidden_layer_sizes = hidden_layer_sizes 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)
+
+ _validate_args(source_table, output_table, summary_table,
independent_varname,
+ dependent_varname, hidden_layer_sizes,
+ optimizer_params, is_classification, weights,
+ warm_start, activation)
+
+ 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
+ classes = []
+ dependent_type = get_expr_type(dependent_varname, source_table)
+ original_dependent_varname = dependent_varname
+ dimension, n_tuples = _tbl_dimension_rownum(
+ schema_madlib, source_table, independent_varname)
+ x_scales = __utils_ind_var_scales(
+ source_table, independent_varname, dimension, schema_madlib)
+ x_means = py_list_to_sql_string(
+ x_scales["mean"], array_type="DOUBLE PRECISION")
+ filtered_stds = [x if x!=0 else 1 for x in x_scales["std"]]
+ x_stds = py_list_to_sql_string(
+ filtered_stds, array_type="DOUBLE PRECISION")
+ 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 += dependent_varname + \
+ "=" + 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
+ else:
+ 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]
+
+ # Need layers sizes before validating for warm_start
+ coeff = []
+ for i in range(len(layer_sizes)-1):
+ fan_in = layer_sizes[i]
+ fan_out = layer_sizes[i+1]
+ span = math.sqrt(6.0/(fan_in+fan_out))
--- End diff --
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