--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_deep_learning.py	Fri Sep 13 12:23:39 2019 -0400
@@ -0,0 +1,370 @@
+import argparse
+import json
+import keras
+import pandas as pd
+import pickle
+import six
+import warnings
+
+from ast import literal_eval
+from keras.models import Sequential, Model
+from galaxy_ml.utils import try_get_attr, get_search_params, SafeEval
+
+
+safe_eval = SafeEval()
+
+
+def _handle_shape(literal):
+    """Eval integer or list/tuple of integers from string
+
+    Parameters:
+    -----------
+    literal : str.
+    """
+    literal = literal.strip()
+    if not literal:
+        return None
+    try:
+        return literal_eval(literal)
+    except NameError as e:
+        print(e)
+        return literal
+
+
+def _handle_regularizer(literal):
+    """Construct regularizer from string literal
+
+    Parameters
+    ----------
+    literal : str. E.g. '(0.1, 0)'
+    """
+    literal = literal.strip()
+    if not literal:
+        return None
+
+    l1, l2 = literal_eval(literal)
+
+    if not l1 and not l2:
+        return None
+
+    if l1 is None:
+        l1 = 0.
+    if l2 is None:
+        l2 = 0.
+
+    return keras.regularizers.l1_l2(l1=l1, l2=l2)
+
+
+def _handle_constraint(config):
+    """Construct constraint from galaxy tool parameters.
+    Suppose correct dictionary format
+
+    Parameters
+    ----------
+    config : dict. E.g.
+        "bias_constraint":
+            {"constraint_options":
+                {"max_value":1.0,
+                "min_value":0.0,
+                "axis":"[0, 1, 2]"
+                },
+            "constraint_type":
+                "MinMaxNorm"
+            }
+    """
+    constraint_type = config['constraint_type']
+    if constraint_type == 'None':
+        return None
+
+    klass = getattr(keras.constraints, constraint_type)
+    options = config.get('constraint_options', {})
+    if 'axis' in options:
+        options['axis'] = literal_eval(options['axis'])
+
+    return klass(**options)
+
+
+def _handle_lambda(literal):
+    return None
+
+
+def _handle_layer_parameters(params):
+    """Access to handle all kinds of parameters
+    """
+    for key, value in six.iteritems(params):
+        if value == 'None':
+            params[key] = None
+            continue
+
+        if type(value) in [int, float, bool]\
+                or (type(value) is str and value.isalpha()):
+            continue
+
+        if key in ['input_shape', 'noise_shape', 'shape', 'batch_shape',
+                   'target_shape', 'dims', 'kernel_size', 'strides',
+                   'dilation_rate', 'output_padding', 'cropping', 'size',
+                   'padding', 'pool_size', 'axis', 'shared_axes'] \
+                and isinstance(value, str):
+            params[key] = _handle_shape(value)
+
+        elif key.endswith('_regularizer') and isinstance(value, dict):
+            params[key] = _handle_regularizer(value)
+
+        elif key.endswith('_constraint') and isinstance(value, dict):
+            params[key] = _handle_constraint(value)
+
+        elif key == 'function':  # No support for lambda/function eval
+            params.pop(key)
+
+    return params
+
+
+def get_sequential_model(config):
+    """Construct keras Sequential model from Galaxy tool parameters
+
+    Parameters:
+    -----------
+    config : dictionary, galaxy tool parameters loaded by JSON
+    """
+    model = Sequential()
+    input_shape = _handle_shape(config['input_shape'])
+    layers = config['layers']
+    for layer in layers:
+        options = layer['layer_selection']
+        layer_type = options.pop('layer_type')
+        klass = getattr(keras.layers, layer_type)
+        kwargs = options.pop('kwargs', '')
+
+        # parameters needs special care
+        options = _handle_layer_parameters(options)
+
+        if kwargs:
+            kwargs = safe_eval('dict(' + kwargs + ')')
+            options.update(kwargs)
+
+        # add input_shape to the first layer only
+        if not getattr(model, '_layers') and input_shape is not None:
+            options['input_shape'] = input_shape
+
+        model.add(klass(**options))
+
+    return model
+
+
+def get_functional_model(config):
+    """Construct keras functional model from Galaxy tool parameters
+
+    Parameters
+    -----------
+    config : dictionary, galaxy tool parameters loaded by JSON
+    """
+    layers = config['layers']
+    all_layers = []
+    for layer in layers:
+        options = layer['layer_selection']
+        layer_type = options.pop('layer_type')
+        klass = getattr(keras.layers, layer_type)
+        inbound_nodes = options.pop('inbound_nodes', None)
+        kwargs = options.pop('kwargs', '')
+
+        # parameters needs special care
+        options = _handle_layer_parameters(options)
+
+        if kwargs:
+            kwargs = safe_eval('dict(' + kwargs + ')')
+            options.update(kwargs)
+
+        # merge layers
+        if 'merging_layers' in options:
+            idxs = literal_eval(options.pop('merging_layers'))
+            merging_layers = [all_layers[i-1] for i in idxs]
+            new_layer = klass(**options)(merging_layers)
+        # non-input layers
+        elif inbound_nodes is not None:
+            new_layer = klass(**options)(all_layers[inbound_nodes-1])
+        # input layers
+        else:
+            new_layer = klass(**options)
+
+        all_layers.append(new_layer)
+
+    input_indexes = _handle_shape(config['input_layers'])
+    input_layers = [all_layers[i-1] for i in input_indexes]
+
+    output_indexes = _handle_shape(config['output_layers'])
+    output_layers = [all_layers[i-1] for i in output_indexes]
+
+    return Model(inputs=input_layers, outputs=output_layers)
+
+
+def get_batch_generator(config):
+    """Construct keras online data generator from Galaxy tool parameters
+
+    Parameters
+    -----------
+    config : dictionary, galaxy tool parameters loaded by JSON
+    """
+    generator_type = config.pop('generator_type')
+    klass = try_get_attr('galaxy_ml.preprocessors', generator_type)
+
+    if generator_type == 'GenomicIntervalBatchGenerator':
+        config['ref_genome_path'] = 'to_be_determined'
+        config['intervals_path'] = 'to_be_determined'
+        config['target_path'] = 'to_be_determined'
+        config['features'] = 'to_be_determined'
+    else:
+        config['fasta_path'] = 'to_be_determined'
+
+    return klass(**config)
+
+
+def config_keras_model(inputs, outfile):
+    """ config keras model layers and output JSON
+
+    Parameters
+    ----------
+    inputs : dict
+        loaded galaxy tool parameters from `keras_model_config`
+        tool.
+    outfile : str
+        Path to galaxy dataset containing keras model JSON.
+    """
+    model_type = inputs['model_selection']['model_type']
+    layers_config = inputs['model_selection']
+
+    if model_type == 'sequential':
+        model = get_sequential_model(layers_config)
+    else:
+        model = get_functional_model(layers_config)
+
+    json_string = model.to_json()
+
+    with open(outfile, 'w') as f:
+        f.write(json_string)
+
+
+def build_keras_model(inputs, outfile, model_json, infile_weights=None,
+                      batch_mode=False, outfile_params=None):
+    """ for `keras_model_builder` tool
+
+    Parameters
+    ----------
+    inputs : dict
+        loaded galaxy tool parameters from `keras_model_builder` tool.
+    outfile : str
+        Path to galaxy dataset containing the keras_galaxy model output.
+    model_json : str
+        Path to dataset containing keras model JSON.
+    infile_weights : str or None
+        If string, path to dataset containing model weights.
+    batch_mode : bool, default=False
+        Whether to build online batch classifier.
+    outfile_params : str, default=None
+        File path to search parameters output.
+    """
+    with open(model_json, 'r') as f:
+        json_model = json.load(f)
+
+    config = json_model['config']
+
+    options = {}
+
+    if json_model['class_name'] == 'Sequential':
+        options['model_type'] = 'sequential'
+        klass = Sequential
+    elif json_model['class_name'] == 'Model':
+        options['model_type'] = 'functional'
+        klass = Model
+    else:
+        raise ValueError("Unknow Keras model class: %s"
+                         % json_model['class_name'])
+
+    # load prefitted model
+    if inputs['mode_selection']['mode_type'] == 'prefitted':
+        estimator = klass.from_config(config)
+        estimator.load_weights(infile_weights)
+    # build train model
+    else:
+        cls_name = inputs['mode_selection']['learning_type']
+        klass = try_get_attr('galaxy_ml.keras_galaxy_models', cls_name)
+
+        options['loss'] = (inputs['mode_selection']
+                           ['compile_params']['loss'])
+        options['optimizer'] =\
+            (inputs['mode_selection']['compile_params']
+             ['optimizer_selection']['optimizer_type']).lower()
+
+        options.update((inputs['mode_selection']['compile_params']
+                        ['optimizer_selection']['optimizer_options']))
+
+        train_metrics = (inputs['mode_selection']['compile_params']
+                         ['metrics']).split(',')
+        if train_metrics[-1] == 'none':
+            train_metrics = train_metrics[:-1]
+        options['metrics'] = train_metrics
+
+        options.update(inputs['mode_selection']['fit_params'])
+        options['seed'] = inputs['mode_selection']['random_seed']
+
+        if batch_mode:
+            generator = get_batch_generator(inputs['mode_selection']
+                                            ['generator_selection'])
+            options['data_batch_generator'] = generator
+            options['prediction_steps'] = \
+                inputs['mode_selection']['prediction_steps']
+            options['class_positive_factor'] = \
+                inputs['mode_selection']['class_positive_factor']
+        estimator = klass(config, **options)
+        if outfile_params:
+            hyper_params = get_search_params(estimator)
+            # TODO: remove this after making `verbose` tunable
+            for h_param in hyper_params:
+                if h_param[1].endswith('verbose'):
+                    h_param[0] = '@'
+            df = pd.DataFrame(hyper_params, columns=['', 'Parameter', 'Value'])
+            df.to_csv(outfile_params, sep='\t', index=False)
+
+    print(repr(estimator))
+    # save model by pickle
+    with open(outfile, 'wb') as f:
+        pickle.dump(estimator, f, pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == '__main__':
+    warnings.simplefilter('ignore')
+
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-m", "--model_json", dest="model_json")
+    aparser.add_argument("-t", "--tool_id", dest="tool_id")
+    aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
+    aparser.add_argument("-o", "--outfile", dest="outfile")
+    aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
+    args = aparser.parse_args()
+
+    input_json_path = args.inputs
+    with open(input_json_path, 'r') as param_handler:
+        inputs = json.load(param_handler)
+
+    tool_id = args.tool_id
+    outfile = args.outfile
+    outfile_params = args.outfile_params
+    model_json = args.model_json
+    infile_weights = args.infile_weights
+
+    # for keras_model_config tool
+    if tool_id == 'keras_model_config':
+        config_keras_model(inputs, outfile)
+
+    # for keras_model_builder tool
+    else:
+        batch_mode = False
+        if tool_id == 'keras_batch_models':
+            batch_mode = True
+
+        build_keras_model(inputs=inputs,
+                          model_json=model_json,
+                          infile_weights=infile_weights,
+                          batch_mode=batch_mode,
+                          outfile=outfile,
+                          outfile_params=outfile_params)