Mercurial > repos > bgruening > sklearn_ensemble
view fitted_model_eval.py @ 32:1a53edc4b438 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 756f8be9c3cd437e131e6410cd625c24fe078e8c"
author | bgruening |
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date | Wed, 22 Jan 2020 08:06:22 -0500 |
parents | 172365bc2b5f |
children | 19d6c2745d34 |
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import argparse import json import pandas as pd import warnings from scipy.io import mmread from sklearn.pipeline import Pipeline from sklearn.metrics.scorer import _check_multimetric_scoring from sklearn.model_selection._validation import _score from galaxy_ml.utils import get_scoring, load_model, read_columns 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, outfile_eval, infile_weights=None, infile1=None, infile2=None): """ Parameter --------- inputs : str File path to galaxy tool parameter infile_estimator : strgit File path to trained estimator input outfile_eval : str File path to save the evalulation results, tabular infile_weights : str File path to weights input infile1 : str File path to dataset containing features infile2 : str File path to dataset containing target values """ warnings.filterwarnings('ignore') with open(inputs, 'r') as param_handler: params = json.load(param_handler) X_test, y_test = _get_X_y(params, infile1, infile2) # load model with open(infile_estimator, 'rb') as est_handler: estimator = load_model(est_handler) main_est = estimator if isinstance(estimator, Pipeline): main_est = estimator.steps[-1][-1] if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'): if not infile_weights or infile_weights == 'None': raise ValueError("The selected model skeleton asks for weights, " "but no dataset for weights was provided!") main_est.load_weights(infile_weights) # handle scorer, convert to scorer dict scoring = params['scoring'] scorer = get_scoring(scoring) scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) if hasattr(estimator, 'evaluate'): scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer, is_multimetric=True) else: scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True) # handle output for name, score in scores.items(): scores[name] = [score] df = pd.DataFrame(scores) df = df[sorted(df.columns)] df.to_csv(path_or_buf=outfile_eval, sep='\t', header=True, index=False) if __name__ == '__main__': aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator") aparser.add_argument("-w", "--infile_weights", dest="infile_weights") aparser.add_argument("-X", "--infile1", dest="infile1") aparser.add_argument("-y", "--infile2", dest="infile2") aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval") args = aparser.parse_args() main(args.inputs, args.infile_estimator, args.outfile_eval, infile_weights=args.infile_weights, infile1=args.infile1, infile2=args.infile2)