annotate simple_model_fit.py @ 14:8a794e6d3388 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad

import argparse
import json
import pickle

import pandas as pd
from galaxy_ml.utils import load_model, read_columns
from scipy.io import mmread
from sklearn.pipeline import Pipeline

N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))


# TODO import from galaxy_ml.utils in future versions
def clean_params(estimator, n_jobs=None):
    """clean unwanted hyperparameter settings

    If n_jobs is not None, set it into the estimator, if applicable

    Return
    ------
    Cleaned estimator object
    """
    ALLOWED_CALLBACKS = (
        "EarlyStopping",
        "TerminateOnNaN",
        "ReduceLROnPlateau",
        "CSVLogger",
        "None",
    )

    estimator_params = estimator.get_params()

    for name, p in estimator_params.items():
        # all potential unauthorized file write
        if name == "memory" or name.endswith("__memory") or name.endswith("_path"):
            new_p = {name: None}
            estimator.set_params(**new_p)
        elif n_jobs is not None and (name == "n_jobs" or name.endswith("__n_jobs")):
            new_p = {name: n_jobs}
            estimator.set_params(**new_p)
        elif name.endswith("callbacks"):
            for cb in p:
                cb_type = cb["callback_selection"]["callback_type"]
                if cb_type not in ALLOWED_CALLBACKS:
                    raise ValueError("Prohibited callback type: %s!" % cb_type)

    return estimator


def _get_X_y(params, infile1, infile2):
    """read from inputs and output X and y

    Parameters
    ----------
    params : dict
        Tool inputs parameter
    infile1 : str
        File path to dataset containing features
    infile2 : str
        File path to dataset containing target values

    """
    # store read dataframe object
    loaded_df = {}

    input_type = params["input_options"]["selected_input"]
    # tabular input
    if input_type == "tabular":
        header = "infer" if params["input_options"]["header1"] else None
        column_option = params["input_options"]["column_selector_options_1"][
            "selected_column_selector_option"
        ]
        if column_option in [
            "by_index_number",
            "all_but_by_index_number",
            "by_header_name",
            "all_but_by_header_name",
        ]:
            c = params["input_options"]["column_selector_options_1"]["col1"]
        else:
            c = None

        df_key = infile1 + repr(header)
        df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
        loaded_df[df_key] = df

        X = read_columns(df, c=c, c_option=column_option).astype(float)
    # sparse input
    elif input_type == "sparse":
        X = mmread(open(infile1, "r"))

    # Get target y
    header = "infer" if params["input_options"]["header2"] else None
    column_option = params["input_options"]["column_selector_options_2"][
        "selected_column_selector_option2"
    ]
    if column_option in [
        "by_index_number",
        "all_but_by_index_number",
        "by_header_name",
        "all_but_by_header_name",
    ]:
        c = params["input_options"]["column_selector_options_2"]["col2"]
    else:
        c = None

    df_key = infile2 + repr(header)
    if df_key in loaded_df:
        infile2 = loaded_df[df_key]
    else:
        infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
        loaded_df[df_key] = infile2

    y = read_columns(
        infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
    )
    if len(y.shape) == 2 and y.shape[1] == 1:
        y = y.ravel()

    return X, y


def main(inputs, infile_estimator, infile1, infile2, out_object, out_weights=None):
    """main

    Parameters
    ----------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : str
        File paths of input estimator

    infile1 : str
        File path to dataset containing features

    infile2 : str
        File path to dataset containing target labels

    out_object : str
        File path for output of fitted model or skeleton

    out_weights : str
        File path for output of weights

    """
    with open(inputs, "r") as param_handler:
        params = json.load(param_handler)

    # load model
    with open(infile_estimator, "rb") as est_handler:
        estimator = load_model(est_handler)
    estimator = clean_params(estimator, n_jobs=N_JOBS)

    X_train, y_train = _get_X_y(params, infile1, infile2)

    estimator.fit(X_train, y_train)

    main_est = estimator
    if isinstance(main_est, Pipeline):
        main_est = main_est.steps[-1][-1]
    if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
        if out_weights:
            main_est.save_weights(out_weights)
        del main_est.model_
        del main_est.fit_params
        del main_est.model_class_
        if getattr(main_est, "validation_data", None):
            del main_est.validation_data
        if getattr(main_est, "data_generator_", None):
            del main_est.data_generator_

    with open(out_object, "wb") as output_handler:
        pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)


if __name__ == "__main__":
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator")
    aparser.add_argument("-y", "--infile1", dest="infile1")
    aparser.add_argument("-g", "--infile2", dest="infile2")
    aparser.add_argument("-o", "--out_object", dest="out_object")
    aparser.add_argument("-t", "--out_weights", dest="out_weights")
    args = aparser.parse_args()

    main(
        args.inputs,
        args.infile_estimator,
        args.infile1,
        args.infile2,
        args.out_object,
        args.out_weights,
    )