Mercurial > repos > bgruening > sklearn_model_fit
view simple_model_fit.py @ 1:49525d37843a draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
author | bgruening |
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date | Thu, 07 Nov 2019 05:31:59 -0500 |
parents | 734c66aa945a |
children | 26decbf4bdb8 |
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import argparse import json import pandas as pd import pickle from galaxy_ml.utils import load_model, read_columns 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_ 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)