comparison keras_deep_learning.py @ 25:b878e4cdd63a draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb

24:b7c3e9a3b954

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.
    """

        print(e)
        return literal


def _handle_regularizer(literal):
    """
    Construct regularizer from string literal

    Parameters
    ----------
    literal : str. E.g. '(0.1, 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.

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)

    return params


def get_sequential_model(config):
    """
    Construct keras Sequential model from Galaxy tool parameters

    Parameters:
    -----------
    config : dictionary, galaxy tool parameters loaded by JSON
    """

        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
    """

    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
    """

    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`

    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)

                ]
            )
        )

        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"]

            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)

        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,
        )

25:b878e4cdd63a

import argparse
import json
import warnings
from ast import literal_eval

import six
from galaxy_ml.model_persist import dump_model_to_h5
from galaxy_ml.utils import SafeEval, try_get_attr
from tensorflow import keras
from tensorflow.keras.models import Model, Sequential

safe_eval = SafeEval()


def _handle_shape(literal):
    """Eval integer or list/tuple of integers from string

    Parameters:
    -----------
    literal : str.
    """

        print(e)
        return literal


def _handle_regularizer(literal):
    """Construct regularizer from string literal

    Parameters
    ----------
    literal : str. E.g. '(0.1, 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.

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)

    return params


def get_sequential_model(config):
    """Construct keras Sequential model from Galaxy tool parameters

    Parameters:
    -----------
    config : dictionary, galaxy tool parameters loaded by JSON
    """

        if kwargs:
            kwargs = safe_eval("dict(" + kwargs + ")")
            options.update(kwargs)

        # add input_shape to the first layer only
        if not model.get_config()["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
    """

    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
    """

    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`

    with open(outfile, "w") as f:
        json.dump(json.loads(json_string), f, indent=2)


def build_keras_model(inputs, outfile, model_json, batch_mode=False):
    """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.
    batch_mode : bool, default=False
        Whether to build online batch classifier.
    """
    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"] == "Functional":
        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)
        raise Exception("Prefitted was deprecated!")
    # build train model
    else:
        cls_name = inputs["mode_selection"]["learning_type"]
        klass = try_get_attr("galaxy_ml.keras_galaxy_models", cls_name)

                ]
            )
        )

        train_metrics = inputs["mode_selection"]["compile_params"]["metrics"]
        if not isinstance(train_metrics, list):  # for older galaxy
            train_metrics = train_metrics.split(",")
        if train_metrics[-1] == "none":
            train_metrics.pop()
        options["metrics"] = train_metrics

        options.update(inputs["mode_selection"]["fit_params"])
        options["seed"] = inputs["mode_selection"]["random_seed"]

            options["prediction_steps"] = inputs["mode_selection"]["prediction_steps"]
            options["class_positive_factor"] = inputs["mode_selection"][
                "class_positive_factor"
            ]
        estimator = klass(config, **options)

    print(repr(estimator))
    # save model
    dump_model_to_h5(estimator, outfile, verbose=1)


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("-o", "--outfile", dest="outfile")
    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
    model_json = args.model_json

    # for keras_model_config tool
    if tool_id == "keras_model_config":
        config_keras_model(inputs, outfile)

        batch_mode = False
        if tool_id == "keras_batch_models":
            batch_mode = True

        build_keras_model(
            inputs=inputs, model_json=model_json, batch_mode=batch_mode, outfile=outfile
        )