--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_deep_learning.py	Sat May 01 01:24:32 2021 +0000
@@ -0,0 +1,412 @@
+import argparse
+import json
+import pickle
+import warnings
+from ast import literal_eval
+
+import keras
+import pandas as pd
+import six
+from galaxy_ml.utils import get_search_params, SafeEval, try_get_attr
+from keras.models import Model, Sequential
+
+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 = 0.0
+    if l2 is None:
+        l2 = 0.0
+
+    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 in ("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 in ("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")
+    if generator_type == "none":
+        return None
+
+    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:
+        json.dump(json.loads(json_string), f, indent=2)
+
+
+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"]
+        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,
+        )