Mercurial > repos > bgruening > keras_train_and_eval
view simple_model_fit.py @ 14:818f9b69d8a0 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a
author | bgruening |
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date | Mon, 02 Oct 2023 10:00:27 +0000 |
parents | b3093f953091 |
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import argparse import json import pandas as pd from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5 from galaxy_ml.utils import 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 estimator = load_model_from_h5(infile_estimator) estimator = clean_params(estimator) 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_ dump_model_to_h5(estimator, out_object) 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, )