Mercurial > repos > bgruening > keras_batch_models
diff simple_model_fit.py @ 3:0a7f2b9e1fcb draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
author | bgruening |
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date | Fri, 01 Nov 2019 17:20:47 -0400 |
parents | |
children | 9afec58b2bf9 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/simple_model_fit.py Fri Nov 01 17:20:47 2019 -0400 @@ -0,0 +1,145 @@ +import argparse +import json +import pandas as pd +import pickle + +from galaxy_ml.utils import load_model, read_columns +from sklearn.pipeline import Pipeline + + +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) + + 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)