Mercurial > repos > bgruening > sklearn_label_encoder
diff fitted_model_eval.py @ 0:3b6ee54eb7e2 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author | bgruening |
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date | Sat, 01 May 2021 00:57:35 +0000 |
parents | |
children | 108141350edb |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/fitted_model_eval.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,183 @@ +import argparse +import json +import warnings + +import pandas as pd +from galaxy_ml.utils import get_scoring, load_model, read_columns +from scipy.io import mmread +from sklearn.metrics.scorer import _check_multimetric_scoring +from sklearn.model_selection._validation import _score +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, + 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 + # Check if scoring is specified + scoring = params["scoring"] + if scoring is not None: + # get_scoring() expects secondary_scoring to be a comma separated string (not a list) + # Check if secondary_scoring is specified + secondary_scoring = scoring.get("secondary_scoring", None) + if secondary_scoring is not None: + # If secondary_scoring is specified, convert the list into comman separated string + scoring["secondary_scoring"] = ",".join(scoring["secondary_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, + )