diff keras_train_and_eval.py @ 0:af2624d5ab32 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_train_and_eval.py	Sat May 01 01:24:32 2021 +0000
@@ -0,0 +1,552 @@
+import argparse
+import json
+import os
+import pickle
+import warnings
+from itertools import chain
+
+import joblib
+import numpy as np
+import pandas as pd
+from galaxy_ml.externals.selene_sdk.utils import compute_score
+from galaxy_ml.keras_galaxy_models import _predict_generator
+from galaxy_ml.model_validations import train_test_split
+from galaxy_ml.utils import (clean_params, get_main_estimator,
+                             get_module, get_scoring, load_model, read_columns,
+                             SafeEval, try_get_attr)
+from scipy.io import mmread
+from sklearn.metrics.scorer import _check_multimetric_scoring
+from sklearn.model_selection import _search, _validation
+from sklearn.model_selection._validation import _score
+from sklearn.pipeline import Pipeline
+from sklearn.utils import indexable, safe_indexing
+
+_fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
+setattr(_search, "_fit_and_score", _fit_and_score)
+setattr(_validation, "_fit_and_score", _fit_and_score)
+
+N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1))
+CACHE_DIR = os.path.join(os.getcwd(), "cached")
+del os
+NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks")
+ALLOWED_CALLBACKS = (
+    "EarlyStopping",
+    "TerminateOnNaN",
+    "ReduceLROnPlateau",
+    "CSVLogger",
+    "None",
+)
+
+
+def _eval_swap_params(params_builder):
+    swap_params = {}
+
+    for p in params_builder["param_set"]:
+        swap_value = p["sp_value"].strip()
+        if swap_value == "":
+            continue
+
+        param_name = p["sp_name"]
+        if param_name.lower().endswith(NON_SEARCHABLE):
+            warnings.warn(
+                "Warning: `%s` is not eligible for search and was "
+                "omitted!" % param_name
+            )
+            continue
+
+        if not swap_value.startswith(":"):
+            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
+            ev = safe_eval(swap_value)
+        else:
+            # Have `:` before search list, asks for estimator evaluatio
+            safe_eval_es = SafeEval(load_estimators=True)
+            swap_value = swap_value[1:].strip()
+            # TODO maybe add regular express check
+            ev = safe_eval_es(swap_value)
+
+        swap_params[param_name] = ev
+
+    return swap_params
+
+
+def train_test_split_none(*arrays, **kwargs):
+    """extend train_test_split to take None arrays
+    and support split by group names.
+    """
+    nones = []
+    new_arrays = []
+    for idx, arr in enumerate(arrays):
+        if arr is None:
+            nones.append(idx)
+        else:
+            new_arrays.append(arr)
+
+    if kwargs["shuffle"] == "None":
+        kwargs["shuffle"] = None
+
+    group_names = kwargs.pop("group_names", None)
+
+    if group_names is not None and group_names.strip():
+        group_names = [name.strip() for name in group_names.split(",")]
+        new_arrays = indexable(*new_arrays)
+        groups = kwargs["labels"]
+        n_samples = new_arrays[0].shape[0]
+        index_arr = np.arange(n_samples)
+        test = index_arr[np.isin(groups, group_names)]
+        train = index_arr[~np.isin(groups, group_names)]
+        rval = list(
+            chain.from_iterable(
+                (safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays
+            )
+        )
+    else:
+        rval = train_test_split(*new_arrays, **kwargs)
+
+    for pos in nones:
+        rval[pos * 2: 2] = [None, None]
+
+    return rval
+
+
+def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):
+    """output scores based on input scorer
+
+    Parameters
+    ----------
+    y_true : array
+        True label or target values
+    pred_probas : array
+        Prediction values, probability for classification problem
+    scorer : dict
+        dict of `sklearn.metrics.scorer.SCORER`
+    is_multimetric : bool, default is True
+    """
+    if y_true.ndim == 1 or y_true.shape[-1] == 1:
+        pred_probas = pred_probas.ravel()
+        pred_labels = (pred_probas > 0.5).astype("int32")
+        targets = y_true.ravel().astype("int32")
+        if not is_multimetric:
+            preds = (
+                pred_labels
+                if scorer.__class__.__name__ == "_PredictScorer"
+                else pred_probas
+            )
+            score = scorer._score_func(targets, preds, **scorer._kwargs)
+
+            return score
+        else:
+            scores = {}
+            for name, one_scorer in scorer.items():
+                preds = (
+                    pred_labels
+                    if one_scorer.__class__.__name__ == "_PredictScorer"
+                    else pred_probas
+                )
+                score = one_scorer._score_func(targets, preds, **one_scorer._kwargs)
+                scores[name] = score
+
+    # TODO: multi-class metrics
+    # multi-label
+    else:
+        pred_labels = (pred_probas > 0.5).astype("int32")
+        targets = y_true.astype("int32")
+        if not is_multimetric:
+            preds = (
+                pred_labels
+                if scorer.__class__.__name__ == "_PredictScorer"
+                else pred_probas
+            )
+            score, _ = compute_score(preds, targets, scorer._score_func)
+            return score
+        else:
+            scores = {}
+            for name, one_scorer in scorer.items():
+                preds = (
+                    pred_labels
+                    if one_scorer.__class__.__name__ == "_PredictScorer"
+                    else pred_probas
+                )
+                score, _ = compute_score(preds, targets, one_scorer._score_func)
+                scores[name] = score
+
+    return scores
+
+
+def main(
+    inputs,
+    infile_estimator,
+    infile1,
+    infile2,
+    outfile_result,
+    outfile_object=None,
+    outfile_weights=None,
+    outfile_y_true=None,
+    outfile_y_preds=None,
+    groups=None,
+    ref_seq=None,
+    intervals=None,
+    targets=None,
+    fasta_path=None,
+):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : str
+        File path to estimator
+
+    infile1 : str
+        File path to dataset containing features
+
+    infile2 : str
+        File path to dataset containing target values
+
+    outfile_result : str
+        File path to save the results, either cv_results or test result
+
+    outfile_object : str, optional
+        File path to save searchCV object
+
+    outfile_weights : str, optional
+        File path to save deep learning model weights
+
+    outfile_y_true : str, optional
+        File path to target values for prediction
+
+    outfile_y_preds : str, optional
+        File path to save deep learning model weights
+
+    groups : str
+        File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
+    """
+    warnings.simplefilter("ignore")
+
+    with open(inputs, "r") as param_handler:
+        params = json.load(param_handler)
+
+    #  load estimator
+    with open(infile_estimator, "rb") as estimator_handler:
+        estimator = load_model(estimator_handler)
+
+    estimator = clean_params(estimator)
+
+    # swap hyperparameter
+    swapping = params["experiment_schemes"]["hyperparams_swapping"]
+    swap_params = _eval_swap_params(swapping)
+    estimator.set_params(**swap_params)
+
+    estimator_params = estimator.get_params()
+
+    # 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"))
+
+    # fasta_file input
+    elif input_type == "seq_fasta":
+        pyfaidx = get_module("pyfaidx")
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        for param in estimator_params.keys():
+            if param.endswith("fasta_path"):
+                estimator.set_params(**{param: fasta_path})
+                break
+        else:
+            raise ValueError(
+                "The selected estimator doesn't support "
+                "fasta file input! Please consider using "
+                "KerasGBatchClassifier with "
+                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+                "in pipeline!"
+            )
+
+    elif input_type == "refseq_and_interval":
+        path_params = {
+            "data_batch_generator__ref_genome_path": ref_seq,
+            "data_batch_generator__intervals_path": intervals,
+            "data_batch_generator__target_path": targets,
+        }
+        estimator.set_params(**path_params)
+        n_intervals = sum(1 for line in open(intervals))
+        X = np.arange(n_intervals)[:, np.newaxis]
+
+    # 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()
+    if input_type == "refseq_and_interval":
+        estimator.set_params(data_batch_generator__features=y.ravel().tolist())
+        y = None
+    # end y
+
+    # load groups
+    if groups:
+        groups_selector = (
+            params["experiment_schemes"]["test_split"]["split_algos"]
+        ).pop("groups_selector")
+
+        header = "infer" if groups_selector["header_g"] else None
+        column_option = groups_selector["column_selector_options_g"][
+            "selected_column_selector_option_g"
+        ]
+        if column_option in [
+            "by_index_number",
+            "all_but_by_index_number",
+            "by_header_name",
+            "all_but_by_header_name",
+        ]:
+            c = groups_selector["column_selector_options_g"]["col_g"]
+        else:
+            c = None
+
+        df_key = groups + repr(header)
+        if df_key in loaded_df:
+            groups = loaded_df[df_key]
+
+        groups = read_columns(
+            groups,
+            c=c,
+            c_option=column_option,
+            sep="\t",
+            header=header,
+            parse_dates=True,
+        )
+        groups = groups.ravel()
+
+    # del loaded_df
+    del loaded_df
+
+    # cache iraps_core fits could increase search speed significantly
+    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
+    main_est = get_main_estimator(estimator)
+    if main_est.__class__.__name__ == "IRAPSClassifier":
+        main_est.set_params(memory=memory)
+
+    # handle scorer, convert to scorer dict
+    scoring = params["experiment_schemes"]["metrics"]["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)
+
+    # handle test (first) split
+    test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]
+
+    if test_split_options["shuffle"] == "group":
+        test_split_options["labels"] = groups
+    if test_split_options["shuffle"] == "stratified":
+        if y is not None:
+            test_split_options["labels"] = y
+        else:
+            raise ValueError(
+                "Stratified shuffle split is not " "applicable on empty target values!"
+            )
+
+    (
+        X_train,
+        X_test,
+        y_train,
+        y_test,
+        groups_train,
+        _groups_test,
+    ) = train_test_split_none(X, y, groups, **test_split_options)
+
+    exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]
+
+    # handle validation (second) split
+    if exp_scheme == "train_val_test":
+        val_split_options = params["experiment_schemes"]["val_split"]["split_algos"]
+
+        if val_split_options["shuffle"] == "group":
+            val_split_options["labels"] = groups_train
+        if val_split_options["shuffle"] == "stratified":
+            if y_train is not None:
+                val_split_options["labels"] = y_train
+            else:
+                raise ValueError(
+                    "Stratified shuffle split is not "
+                    "applicable on empty target values!"
+                )
+
+        (
+            X_train,
+            X_val,
+            y_train,
+            y_val,
+            groups_train,
+            _groups_val,
+        ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)
+
+    # train and eval
+    if hasattr(estimator, "validation_data"):
+        if exp_scheme == "train_val_test":
+            estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
+        else:
+            estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
+    else:
+        estimator.fit(X_train, y_train)
+
+    if hasattr(estimator, "evaluate"):
+        steps = estimator.prediction_steps
+        batch_size = estimator.batch_size
+        generator = estimator.data_generator_.flow(
+            X_test, y=y_test, batch_size=batch_size
+        )
+        predictions, y_true = _predict_generator(
+            estimator.model_, generator, steps=steps
+        )
+        scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)
+
+    else:
+        if hasattr(estimator, "predict_proba"):
+            predictions = estimator.predict_proba(X_test)
+        else:
+            predictions = estimator.predict(X_test)
+
+        y_true = y_test
+        scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
+    if outfile_y_true:
+        try:
+            pd.DataFrame(y_true).to_csv(outfile_y_true, sep="\t", index=False)
+            pd.DataFrame(predictions).astype(np.float32).to_csv(
+                outfile_y_preds,
+                sep="\t",
+                index=False,
+                float_format="%g",
+                chunksize=10000,
+            )
+        except Exception as e:
+            print("Error in saving predictions: %s" % e)
+
+    # 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_result, sep="\t", header=True, index=False)
+
+    memory.clear(warn=False)
+
+    if outfile_object:
+        main_est = estimator
+        if isinstance(estimator, Pipeline):
+            main_est = estimator.steps[-1][-1]
+
+        if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
+            if outfile_weights:
+                main_est.save_weights(outfile_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_
+
+        with open(outfile_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("-e", "--estimator", dest="infile_estimator")
+    aparser.add_argument("-X", "--infile1", dest="infile1")
+    aparser.add_argument("-y", "--infile2", dest="infile2")
+    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
+    aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
+    aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
+    aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    args = aparser.parse_args()
+
+    main(
+        args.inputs,
+        args.infile_estimator,
+        args.infile1,
+        args.infile2,
+        args.outfile_result,
+        outfile_object=args.outfile_object,
+        outfile_weights=args.outfile_weights,
+        outfile_y_true=args.outfile_y_true,
+        outfile_y_preds=args.outfile_y_preds,
+        groups=args.groups,
+        ref_seq=args.ref_seq,
+        intervals=args.intervals,
+        targets=args.targets,
+        fasta_path=args.fasta_path,
+    )