# HG changeset patch # User bgruening # Date 1619830655 0 # Node ID 3b6ee54eb7e228fa17333285a98c51e22f963fde "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717" diff -r 000000000000 -r 3b6ee54eb7e2 README.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.rst Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,146 @@ +Galaxy wrapper for scikit-learn library +*************************************** + +Contents +======== + +- `What is scikit-learn?`_ + - `Scikit-learn main package groups`_ + - `Tools offered by this wrapper`_ + +- `Machine learning workflows`_ + - `Supervised learning workflows`_ + - `Unsupervised learning workflows`_ + + +____________________________ + + +.. _What is scikit-learn?: + +What is scikit-learn? +===================== + +Scikit-learn is an open-source machine learning library for the Python programming language. It offers various algorithms for performing supervised and unsupervised learning as well as data preprocessing and transformation, model selection and evaluation, and dataset utilities. It is built upon SciPy (Scientific Python) library. + +Scikit-learn source code can be accessed at https://github.com/scikit-learn/scikit-learn. +Detailed installation instructions can be found at http://scikit-learn.org/stable/install.html + + +.. _Scikit-learn main package groups: + +Scikit-learn main package groups +================================ + +Scikit-learn provides the users with several main groups of related operations. +These are: + +- Classification + - Identifying to which category an object belongs. +- Regression + - Predicting a continuous-valued attribute associated with an object. +- Clustering + - Automatic grouping of similar objects into sets. +- Preprocessing + - Feature extraction and normalization. +- Model selection and evaluation + - Comparing, validating and choosing parameters and models. +- Dimensionality reduction + - Reducing the number of random variables to consider. + +Each group consists of a number of well-known algorithms from the category. For example, one can find hierarchical, spectral, kmeans, and other clustering methods in sklearn.cluster package. + + +.. _Tools offered by this wrapper: + +Available tools in the current wrapper +====================================== + +The current release of the wrapper offers a subset of the packages from scikit-learn library. You can find: + +- A subset of classification metric functions +- Linear and quadratic discriminant classifiers +- Random forest and Ada boost classifiers and regressors +- All the clustering methods +- All support vector machine classifiers +- A subset of data preprocessing estimator classes +- Pairwise metric measurement functions + +In addition, several tools for performing matrix operations, generating problem-specific datasets, and encoding text and extracting features have been prepared to help the user with more advanced operations. + +.. _Machine learning workflows: + +Machine learning workflows +========================== + +Machine learning is about processes. No matter what machine learning algorithm we use, we can apply typical workflows and dataflows to produce more robust models and better predictions. +Here we discuss supervised and unsupervised learning workflows. + +.. _Supervised learning workflows: + +Supervised machine learning workflows +===================================== + +**What is supervised learning?** + +In this machine learning task, given sample data which are labeled, the aim is to build a model which can predict the labels for new observations. +In practice, there are five steps which we can go through to start from raw input data and end up getting reasonable predictions for new samples: + +1. Preprocess the data:: + + * Change the collected data into the proper format and datatype. + * Adjust the data quality by filling the missing values, performing + required scaling and normalizations, etc. + * Extract features which are the most meaningfull for the learning task. + * Split the ready dataset into training and test samples. + +2. Choose an algorithm:: + + * These factors help one to choose a learning algorithm: + - Nature of the data (e.g. linear vs. nonlinear data) + - Structure of the predicted output (e.g. binary vs. multilabel classification) + - Memory and time usage of the training + - Predictive accuracy on new data + - Interpretability of the predictions + +3. Choose a validation method + + Every machine learning model should be evaluated before being put into practicical use. + There are numerous performance metrics to evaluate machine learning models. + For supervised learning, usually classification or regression metrics are used. + + A validation method helps to evaluate the performance metrics of a trained model in order + to optimize its performance or ultimately switch to a more efficient model. + Cross-validation is a known validation method. + +4. Fit a model + + Given the learning algorithm, validation method, and performance metric(s) + repeat the following steps:: + + * Train the model. + * Evaluate based on metrics. + * Optimize unitl satisfied. + +5. Use fitted model for prediction:: + + This is a final evaluation in which, the optimized model is used to make predictions + on unseen (here test) samples. After this, the model is put into production. + +.. _Unsupervised learning workflows: + +Unsupervised machine learning workflows +======================================= + +**What is unsupervised learning?** + +Unlike supervised learning and more liklely in real life, here the initial data is not labeled. +The task is to extract the structure from the data and group the samples based on their similarities. +Clustering and dimensionality reduction are two famous examples of unsupervised learning tasks. + +In this case, the workflow is as follows:: + + * Preprocess the data (without splitting to train and test). + * Train a model. + * Evaluate and tune parameters. + * Analyse the model and test on real data. diff -r 000000000000 -r 3b6ee54eb7e2 association_rules.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/association_rules.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,116 @@ +import argparse +import json +import warnings + +import pandas as pd +from mlxtend.frequent_patterns import association_rules, fpgrowth +from mlxtend.preprocessing import TransactionEncoder + + +def main(inputs, infile, outfile, min_support=0.5, min_confidence=0.5, min_lift=1.0, min_conviction=1.0, max_length=None): + """ + Parameter + --------- + input : str + File path to galaxy tool parameter + + infile : str + File paths of input vector + + outfile : str + File path to output matrix + + min_support: float + Minimum support + + min_confidence: float + Minimum confidence + + min_lift: float + Minimum lift + + min_conviction: float + Minimum conviction + + max_length: int + Maximum length + + """ + warnings.simplefilter('ignore') + + with open(inputs, 'r') as param_handler: + params = json.load(param_handler) + + input_header = params['header0'] + header = 'infer' if input_header else None + + with open(infile) as fp: + lines = fp.read().splitlines() + + if header is not None: + lines = lines[1:] + + dataset = [] + for line in lines: + line_items = line.split("\t") + dataset.append(line_items) + + # TransactionEncoder learns the unique labels in the dataset and transforms the + # input dataset (a Python list of lists) into a one-hot encoded NumPy boolean array + te = TransactionEncoder() + te_ary = te.fit_transform(dataset) + + # Turn the encoded NumPy array into a DataFrame + df = pd.DataFrame(te_ary, columns=te.columns_) + + # Extract frequent itemsets for association rule mining + # use_colnames: Use DataFrames' column names in the returned DataFrame instead of column indices + frequent_itemsets = fpgrowth(df, min_support=min_support, use_colnames=True, max_len=max_length) + + # Get association rules, with confidence larger than min_confidence + rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=min_confidence) + + # Filter association rules, keeping rules with lift and conviction larger than min_liftand and min_conviction + rules = rules[(rules['lift'] >= min_lift) & (rules['conviction'] >= min_conviction)] + + # Convert columns from frozenset to list (more readable) + rules['antecedents'] = rules['antecedents'].apply(list) + rules['consequents'] = rules['consequents'].apply(list) + + # The next 3 steps are intended to fix the order of the association + # rules generated, so tests that rely on diff'ing a desired output + # with an expected output can pass + + # 1) Sort entry in every row/column for columns 'antecedents' and 'consequents' + rules['antecedents'] = rules['antecedents'].apply(lambda row: sorted(row)) + rules['consequents'] = rules['consequents'].apply(lambda row: sorted(row)) + + # 2) Create two temporary string columns to sort on + rules['ant_str'] = rules['antecedents'].apply(lambda row: " ".join(row)) + rules['con_str'] = rules['consequents'].apply(lambda row: " ".join(row)) + + # 3) Sort results so they are re-producable + rules.sort_values(by=['ant_str', 'con_str'], inplace=True) + del rules['ant_str'] + del rules['con_str'] + rules.reset_index(drop=True, inplace=True) + + # Write association rules and metrics to file + rules.to_csv(outfile, sep="\t", index=False) + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-y", "--infile", dest="infile", required=True) + aparser.add_argument("-o", "--outfile", dest="outfile", required=True) + aparser.add_argument("-s", "--support", dest="support", default=0.5) + aparser.add_argument("-c", "--confidence", dest="confidence", default=0.5) + aparser.add_argument("-l", "--lift", dest="lift", default=1.0) + aparser.add_argument("-v", "--conviction", dest="conviction", default=1.0) + aparser.add_argument("-t", "--length", dest="length", default=5) + args = aparser.parse_args() + + main(args.inputs, args.infile, args.outfile, + min_support=float(args.support), min_confidence=float(args.confidence), + min_lift=float(args.lift), min_conviction=float(args.conviction), max_length=int(args.length)) diff -r 000000000000 -r 3b6ee54eb7e2 fitted_model_eval.py --- /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, + ) diff -r 000000000000 -r 3b6ee54eb7e2 keras_deep_learning.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/keras_deep_learning.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,412 @@ +import argparse +import json +import pickle +import warnings +from ast import literal_eval + +import keras +import pandas as pd +import six +from galaxy_ml.utils import get_search_params, SafeEval, try_get_attr +from keras.models import Model, Sequential + +safe_eval = SafeEval() + + +def _handle_shape(literal): + """ + Eval integer or list/tuple of integers from string + + Parameters: + ----------- + literal : str. + """ + literal = literal.strip() + if not literal: + return None + try: + return literal_eval(literal) + except NameError as e: + print(e) + return literal + + +def _handle_regularizer(literal): + """ + Construct regularizer from string literal + + Parameters + ---------- + literal : str. E.g. '(0.1, 0)' + """ + literal = literal.strip() + if not literal: + return None + + l1, l2 = literal_eval(literal) + + if not l1 and not l2: + return None + + if l1 is None: + l1 = 0.0 + if l2 is None: + l2 = 0.0 + + return keras.regularizers.l1_l2(l1=l1, l2=l2) + + +def _handle_constraint(config): + """ + Construct constraint from galaxy tool parameters. + Suppose correct dictionary format + + Parameters + ---------- + config : dict. E.g. + "bias_constraint": + {"constraint_options": + {"max_value":1.0, + "min_value":0.0, + "axis":"[0, 1, 2]" + }, + "constraint_type": + "MinMaxNorm" + } + """ + constraint_type = config["constraint_type"] + if constraint_type in ("None", ""): + return None + + klass = getattr(keras.constraints, constraint_type) + options = config.get("constraint_options", {}) + if "axis" in options: + options["axis"] = literal_eval(options["axis"]) + + return klass(**options) + + +def _handle_lambda(literal): + return None + + +def _handle_layer_parameters(params): + """ + Access to handle all kinds of parameters + """ + for key, value in six.iteritems(params): + if value in ("None", ""): + params[key] = None + continue + + if type(value) in [int, float, bool] or ( + type(value) is str and value.isalpha() + ): + continue + + if ( + key + in [ + "input_shape", + "noise_shape", + "shape", + "batch_shape", + "target_shape", + "dims", + "kernel_size", + "strides", + "dilation_rate", + "output_padding", + "cropping", + "size", + "padding", + "pool_size", + "axis", + "shared_axes", + ] + and isinstance(value, str) + ): + params[key] = _handle_shape(value) + + elif key.endswith("_regularizer") and isinstance(value, dict): + params[key] = _handle_regularizer(value) + + elif key.endswith("_constraint") and isinstance(value, dict): + params[key] = _handle_constraint(value) + + elif key == "function": # No support for lambda/function eval + params.pop(key) + + return params + + +def get_sequential_model(config): + """ + Construct keras Sequential model from Galaxy tool parameters + + Parameters: + ----------- + config : dictionary, galaxy tool parameters loaded by JSON + """ + model = Sequential() + input_shape = _handle_shape(config["input_shape"]) + layers = config["layers"] + for layer in layers: + options = layer["layer_selection"] + layer_type = options.pop("layer_type") + klass = getattr(keras.layers, layer_type) + kwargs = options.pop("kwargs", "") + + # parameters needs special care + options = _handle_layer_parameters(options) + + if kwargs: + kwargs = safe_eval("dict(" + kwargs + ")") + options.update(kwargs) + + # add input_shape to the first layer only + if not getattr(model, "_layers") and input_shape is not None: + options["input_shape"] = input_shape + + model.add(klass(**options)) + + return model + + +def get_functional_model(config): + """ + Construct keras functional model from Galaxy tool parameters + + Parameters + ----------- + config : dictionary, galaxy tool parameters loaded by JSON + """ + layers = config["layers"] + all_layers = [] + for layer in layers: + options = layer["layer_selection"] + layer_type = options.pop("layer_type") + klass = getattr(keras.layers, layer_type) + inbound_nodes = options.pop("inbound_nodes", None) + kwargs = options.pop("kwargs", "") + + # parameters needs special care + options = _handle_layer_parameters(options) + + if kwargs: + kwargs = safe_eval("dict(" + kwargs + ")") + options.update(kwargs) + + # merge layers + if "merging_layers" in options: + idxs = literal_eval(options.pop("merging_layers")) + merging_layers = [all_layers[i - 1] for i in idxs] + new_layer = klass(**options)(merging_layers) + # non-input layers + elif inbound_nodes is not None: + new_layer = klass(**options)(all_layers[inbound_nodes - 1]) + # input layers + else: + new_layer = klass(**options) + + all_layers.append(new_layer) + + input_indexes = _handle_shape(config["input_layers"]) + input_layers = [all_layers[i - 1] for i in input_indexes] + + output_indexes = _handle_shape(config["output_layers"]) + output_layers = [all_layers[i - 1] for i in output_indexes] + + return Model(inputs=input_layers, outputs=output_layers) + + +def get_batch_generator(config): + """ + Construct keras online data generator from Galaxy tool parameters + + Parameters + ----------- + config : dictionary, galaxy tool parameters loaded by JSON + """ + generator_type = config.pop("generator_type") + if generator_type == "none": + return None + + klass = try_get_attr("galaxy_ml.preprocessors", generator_type) + + if generator_type == "GenomicIntervalBatchGenerator": + config["ref_genome_path"] = "to_be_determined" + config["intervals_path"] = "to_be_determined" + config["target_path"] = "to_be_determined" + config["features"] = "to_be_determined" + else: + config["fasta_path"] = "to_be_determined" + + return klass(**config) + + +def config_keras_model(inputs, outfile): + """ + config keras model layers and output JSON + + Parameters + ---------- + inputs : dict + loaded galaxy tool parameters from `keras_model_config` + tool. + outfile : str + Path to galaxy dataset containing keras model JSON. + """ + model_type = inputs["model_selection"]["model_type"] + layers_config = inputs["model_selection"] + + if model_type == "sequential": + model = get_sequential_model(layers_config) + else: + model = get_functional_model(layers_config) + + json_string = model.to_json() + + with open(outfile, "w") as f: + json.dump(json.loads(json_string), f, indent=2) + + +def build_keras_model( + inputs, + outfile, + model_json, + infile_weights=None, + batch_mode=False, + outfile_params=None, +): + """ + for `keras_model_builder` tool + + Parameters + ---------- + inputs : dict + loaded galaxy tool parameters from `keras_model_builder` tool. + outfile : str + Path to galaxy dataset containing the keras_galaxy model output. + model_json : str + Path to dataset containing keras model JSON. + infile_weights : str or None + If string, path to dataset containing model weights. + batch_mode : bool, default=False + Whether to build online batch classifier. + outfile_params : str, default=None + File path to search parameters output. + """ + with open(model_json, "r") as f: + json_model = json.load(f) + + config = json_model["config"] + + options = {} + + if json_model["class_name"] == "Sequential": + options["model_type"] = "sequential" + klass = Sequential + elif json_model["class_name"] == "Model": + options["model_type"] = "functional" + klass = Model + else: + raise ValueError("Unknow Keras model class: %s" % json_model["class_name"]) + + # load prefitted model + if inputs["mode_selection"]["mode_type"] == "prefitted": + estimator = klass.from_config(config) + estimator.load_weights(infile_weights) + # build train model + else: + cls_name = inputs["mode_selection"]["learning_type"] + klass = try_get_attr("galaxy_ml.keras_galaxy_models", cls_name) + + options["loss"] = inputs["mode_selection"]["compile_params"]["loss"] + options["optimizer"] = ( + inputs["mode_selection"]["compile_params"]["optimizer_selection"][ + "optimizer_type" + ] + ).lower() + + options.update( + ( + inputs["mode_selection"]["compile_params"]["optimizer_selection"][ + "optimizer_options" + ] + ) + ) + + train_metrics = inputs["mode_selection"]["compile_params"]["metrics"] + if train_metrics[-1] == "none": + train_metrics = train_metrics[:-1] + options["metrics"] = train_metrics + + options.update(inputs["mode_selection"]["fit_params"]) + options["seed"] = inputs["mode_selection"]["random_seed"] + + if batch_mode: + generator = get_batch_generator( + inputs["mode_selection"]["generator_selection"] + ) + options["data_batch_generator"] = generator + options["prediction_steps"] = inputs["mode_selection"]["prediction_steps"] + options["class_positive_factor"] = inputs["mode_selection"][ + "class_positive_factor" + ] + estimator = klass(config, **options) + if outfile_params: + hyper_params = get_search_params(estimator) + # TODO: remove this after making `verbose` tunable + for h_param in hyper_params: + if h_param[1].endswith("verbose"): + h_param[0] = "@" + df = pd.DataFrame(hyper_params, columns=["", "Parameter", "Value"]) + df.to_csv(outfile_params, sep="\t", index=False) + + print(repr(estimator)) + # save model by pickle + with open(outfile, "wb") as f: + pickle.dump(estimator, f, pickle.HIGHEST_PROTOCOL) + + +if __name__ == "__main__": + warnings.simplefilter("ignore") + + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-m", "--model_json", dest="model_json") + aparser.add_argument("-t", "--tool_id", dest="tool_id") + aparser.add_argument("-w", "--infile_weights", dest="infile_weights") + aparser.add_argument("-o", "--outfile", dest="outfile") + aparser.add_argument("-p", "--outfile_params", dest="outfile_params") + args = aparser.parse_args() + + input_json_path = args.inputs + with open(input_json_path, "r") as param_handler: + inputs = json.load(param_handler) + + tool_id = args.tool_id + outfile = args.outfile + outfile_params = args.outfile_params + model_json = args.model_json + infile_weights = args.infile_weights + + # for keras_model_config tool + if tool_id == "keras_model_config": + config_keras_model(inputs, outfile) + + # for keras_model_builder tool + else: + batch_mode = False + if tool_id == "keras_batch_models": + batch_mode = True + + build_keras_model( + inputs=inputs, + model_json=model_json, + infile_weights=infile_weights, + batch_mode=batch_mode, + outfile=outfile, + outfile_params=outfile_params, + ) diff -r 000000000000 -r 3b6ee54eb7e2 keras_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/keras_macros.xml Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,985 @@ + + 0.5.0 + + + + + + + + +
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+ + + +
+ + + + + + + +
+ + + + + + @misc{chollet2015keras, + title={Keras}, + url={https://keras.io}, + author={Chollet, Fran\c{c}ois and others}, + year={2015}, + howpublished={https://keras.io}, + } + + + + + + @misc{tensorflow2015-whitepaper, + title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems}, + url={https://www.tensorflow.org/}, + note={Software available from tensorflow.org}, + author={ + Mart\'{\i}n~Abadi and + Ashish~Agarwal and + Paul~Barham and + Eugene~Brevdo and + Zhifeng~Chen and + Craig~Citro and + Greg~S.~Corrado and + Andy~Davis and + Jeffrey~Dean and + Matthieu~Devin and + Sanjay~Ghemawat and + Ian~Goodfellow and + Andrew~Harp and + Geoffrey~Irving and + Michael~Isard and + Yangqing Jia and + Rafal~Jozefowicz and + Lukasz~Kaiser and + Manjunath~Kudlur and + Josh~Levenberg and + Dandelion~Man\'{e} and + Rajat~Monga and + Sherry~Moore and + Derek~Murray and + Chris~Olah and + Mike~Schuster and + Jonathon~Shlens and + Benoit~Steiner and + Ilya~Sutskever and + Kunal~Talwar and + Paul~Tucker and + Vincent~Vanhoucke and + Vijay~Vasudevan and + Fernanda~Vi\'{e}gas and + Oriol~Vinyals and + Pete~Warden and + Martin~Wattenberg and + Martin~Wicke and + Yuan~Yu and + Xiaoqiang~Zheng}, + year={2015}, + } + + + + \ No newline at end of file diff -r 000000000000 -r 3b6ee54eb7e2 keras_train_and_eval.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/keras_train_and_eval.py Sat May 01 00:57:35 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, + ) diff -r 000000000000 -r 3b6ee54eb7e2 label_encoder.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/label_encoder.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,48 @@ +import argparse +import json +import warnings + +import numpy as np +import pandas as pd +from sklearn.preprocessing import LabelEncoder + + +def main(inputs, infile, outfile): + """ + Parameter + --------- + input : str + File path to galaxy tool parameter + + infile : str + File paths of input vector + + outfile : str + File path to output vector + + """ + warnings.simplefilter('ignore') + + with open(inputs, 'r') as param_handler: + params = json.load(param_handler) + + input_header = params['header0'] + header = 'infer' if input_header else None + + input_vector = pd.read_csv(infile, sep='\t', header=header) + + le = LabelEncoder() + + output_vector = le.fit_transform(input_vector) + + np.savetxt(outfile, output_vector, fmt="%d", delimiter='\t') + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-y", "--infile", dest="infile") + aparser.add_argument("-o", "--outfile", dest="outfile") + args = aparser.parse_args() + + main(args.inputs, args.infile, args.outfile) diff -r 000000000000 -r 3b6ee54eb7e2 label_encoder.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/label_encoder.xml Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,65 @@ + + Encode target labels with value between 0 and n_classes-1 + + main_macros.xml + + + + echo "@VERSION@" + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r 000000000000 -r 3b6ee54eb7e2 main_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main_macros.xml Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2015 @@ + + 1.0.8.3 + + + + Galaxy-ML + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + selected_tasks['selected_task'] == 'load' + + + selected_tasks['selected_task'] == 'train' + + + + + + + + 10.5281/zenodo.15094 + + + + + + + @article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011} + } + + + + + + + + + @Misc{, + author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, + title = {{SciPy}: Open source scientific tools for {Python}}, + year = {2001--}, + url = "http://www.scipy.org/", + note = {[Online; accessed 2016-04-09]} + } + + + + + + + @article{DBLP:journals/corr/abs-1711-08477, + author = {Ryan J. Urbanowicz and + Randal S. Olson and + Peter Schmitt and + Melissa Meeker and + Jason H. Moore}, + title = {Benchmarking Relief-Based Feature Selection Methods}, + journal = {CoRR}, + volume = {abs/1711.08477}, + year = {2017}, + url = {http://arxiv.org/abs/1711.08477}, + archivePrefix = {arXiv}, + eprint = {1711.08477}, + timestamp = {Mon, 13 Aug 2018 16:46:04 +0200}, + biburl = {https://dblp.org/rec/bib/journals/corr/abs-1711-08477}, + bibsource = {dblp computer science bibliography, https://dblp.org} + } + + + + + + @inproceedings{Chen:2016:XST:2939672.2939785, + author = {Chen, Tianqi and Guestrin, Carlos}, + title = {{XGBoost}: A Scalable Tree Boosting System}, + booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, + series = {KDD '16}, + year = {2016}, + isbn = {978-1-4503-4232-2}, + location = {San Francisco, California, USA}, + pages = {785--794}, + numpages = {10}, + url = {http://doi.acm.org/10.1145/2939672.2939785}, + doi = {10.1145/2939672.2939785}, + acmid = {2939785}, + publisher = {ACM}, + address = {New York, NY, USA}, + keywords = {large-scale machine learning}, + } + + + + + + @article{JMLR:v18:16-365, + author = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas}, + title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning}, + journal = {Journal of Machine Learning Research}, + year = {2017}, + volume = {18}, + number = {17}, + pages = {1-5}, + url = {http://jmlr.org/papers/v18/16-365.html} + } + + + + + + @article{chen2019selene, title={Selene: a PyTorch-based deep learning library for sequence data}, author={Chen, Kathleen M and Cofer, Evan M and Zhou, Jian and Troyanskaya, Olga G}, journal={Nature methods}, volume={16}, number={4}, pages={315}, year={2019}, publisher={Nature Publishing Group} + } + + + + diff -r 000000000000 -r 3b6ee54eb7e2 ml_visualization_ex.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ml_visualization_ex.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,645 @@ +import argparse +import json +import os +import warnings + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import plotly +import plotly.graph_objs as go +from galaxy_ml.utils import load_model, read_columns, SafeEval +from keras.models import model_from_json +from keras.utils import plot_model +from sklearn.feature_selection.base import SelectorMixin +from sklearn.metrics import (auc, average_precision_score, confusion_matrix, + precision_recall_curve, roc_curve) +from sklearn.pipeline import Pipeline + +safe_eval = SafeEval() + +# plotly default colors +default_colors = [ + "#1f77b4", # muted blue + "#ff7f0e", # safety orange + "#2ca02c", # cooked asparagus green + "#d62728", # brick red + "#9467bd", # muted purple + "#8c564b", # chestnut brown + "#e377c2", # raspberry yogurt pink + "#7f7f7f", # middle gray + "#bcbd22", # curry yellow-green + "#17becf", # blue-teal +] + + +def visualize_pr_curve_plotly(df1, df2, pos_label, title=None): + """output pr-curve in html using plotly + + df1 : pandas.DataFrame + Containing y_true + df2 : pandas.DataFrame + Containing y_score + pos_label : None + The label of positive class + title : str + Plot title + """ + data = [] + for idx in range(df1.shape[1]): + y_true = df1.iloc[:, idx].values + y_score = df2.iloc[:, idx].values + + precision, recall, _ = precision_recall_curve( + y_true, y_score, pos_label=pos_label + ) + ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1) + + trace = go.Scatter( + x=recall, + y=precision, + mode="lines", + marker=dict(color=default_colors[idx % len(default_colors)]), + name="%s (area = %.3f)" % (idx, ap), + ) + data.append(trace) + + layout = go.Layout( + xaxis=dict(title="Recall", linecolor="lightslategray", linewidth=1), + yaxis=dict(title="Precision", linecolor="lightslategray", linewidth=1), + title=dict( + text=title or "Precision-Recall Curve", + x=0.5, + y=0.92, + xanchor="center", + yanchor="top", + ), + font=dict(family="sans-serif", size=11), + # control backgroud colors + plot_bgcolor="rgba(255,255,255,0)", + ) + """ + legend=dict( + x=0.95, + y=0, + traceorder="normal", + font=dict( + family="sans-serif", + size=9, + color="black" + ), + bgcolor="LightSteelBlue", + bordercolor="Black", + borderwidth=2 + ),""" + + fig = go.Figure(data=data, layout=layout) + + plotly.offline.plot(fig, filename="output.html", auto_open=False) + # to be discovered by `from_work_dir` + os.rename("output.html", "output") + + +def visualize_pr_curve_matplotlib(df1, df2, pos_label, title=None): + """visualize pr-curve using matplotlib and output svg image""" + backend = matplotlib.get_backend() + if "inline" not in backend: + matplotlib.use("SVG") + plt.style.use("seaborn-colorblind") + plt.figure() + + for idx in range(df1.shape[1]): + y_true = df1.iloc[:, idx].values + y_score = df2.iloc[:, idx].values + + precision, recall, _ = precision_recall_curve( + y_true, y_score, pos_label=pos_label + ) + ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1) + + plt.step( + recall, + precision, + "r-", + color="black", + alpha=0.3, + lw=1, + where="post", + label="%s (area = %.3f)" % (idx, ap), + ) + + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel("Recall") + plt.ylabel("Precision") + title = title or "Precision-Recall Curve" + plt.title(title) + folder = os.getcwd() + plt.savefig(os.path.join(folder, "output.svg"), format="svg") + os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output")) + + +def visualize_roc_curve_plotly(df1, df2, pos_label, drop_intermediate=True, title=None): + """output roc-curve in html using plotly + + df1 : pandas.DataFrame + Containing y_true + df2 : pandas.DataFrame + Containing y_score + pos_label : None + The label of positive class + drop_intermediate : bool + Whether to drop some suboptimal thresholds + title : str + Plot title + """ + data = [] + for idx in range(df1.shape[1]): + y_true = df1.iloc[:, idx].values + y_score = df2.iloc[:, idx].values + + fpr, tpr, _ = roc_curve( + y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate + ) + roc_auc = auc(fpr, tpr) + + trace = go.Scatter( + x=fpr, + y=tpr, + mode="lines", + marker=dict(color=default_colors[idx % len(default_colors)]), + name="%s (area = %.3f)" % (idx, roc_auc), + ) + data.append(trace) + + layout = go.Layout( + xaxis=dict( + title="False Positive Rate", linecolor="lightslategray", linewidth=1 + ), + yaxis=dict(title="True Positive Rate", linecolor="lightslategray", linewidth=1), + title=dict( + text=title or "Receiver Operating Characteristic (ROC) Curve", + x=0.5, + y=0.92, + xanchor="center", + yanchor="top", + ), + font=dict(family="sans-serif", size=11), + # control backgroud colors + plot_bgcolor="rgba(255,255,255,0)", + ) + """ + # legend=dict( + # x=0.95, + # y=0, + # traceorder="normal", + # font=dict( + # family="sans-serif", + # size=9, + # color="black" + # ), + # bgcolor="LightSteelBlue", + # bordercolor="Black", + # borderwidth=2 + # ), + """ + + fig = go.Figure(data=data, layout=layout) + + plotly.offline.plot(fig, filename="output.html", auto_open=False) + # to be discovered by `from_work_dir` + os.rename("output.html", "output") + + +def visualize_roc_curve_matplotlib( + df1, df2, pos_label, drop_intermediate=True, title=None +): + """visualize roc-curve using matplotlib and output svg image""" + backend = matplotlib.get_backend() + if "inline" not in backend: + matplotlib.use("SVG") + plt.style.use("seaborn-colorblind") + plt.figure() + + for idx in range(df1.shape[1]): + y_true = df1.iloc[:, idx].values + y_score = df2.iloc[:, idx].values + + fpr, tpr, _ = roc_curve( + y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate + ) + roc_auc = auc(fpr, tpr) + + plt.step( + fpr, + tpr, + "r-", + color="black", + alpha=0.3, + lw=1, + where="post", + label="%s (area = %.3f)" % (idx, roc_auc), + ) + + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel("False Positive Rate") + plt.ylabel("True Positive Rate") + title = title or "Receiver Operating Characteristic (ROC) Curve" + plt.title(title) + folder = os.getcwd() + plt.savefig(os.path.join(folder, "output.svg"), format="svg") + os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output")) + + +def get_dataframe(file_path, plot_selection, header_name, column_name): + header = "infer" if plot_selection[header_name] else None + column_option = plot_selection[column_name]["selected_column_selector_option"] + if column_option in [ + "by_index_number", + "all_but_by_index_number", + "by_header_name", + "all_but_by_header_name", + ]: + col = plot_selection[column_name]["col1"] + else: + col = None + _, input_df = read_columns( + file_path, + c=col, + c_option=column_option, + return_df=True, + sep="\t", + header=header, + parse_dates=True, + ) + return input_df + + +def main( + inputs, + infile_estimator=None, + infile1=None, + infile2=None, + outfile_result=None, + outfile_object=None, + groups=None, + ref_seq=None, + intervals=None, + targets=None, + fasta_path=None, + model_config=None, + true_labels=None, + predicted_labels=None, + plot_color=None, + title=None, +): + """ + Parameter + --------- + inputs : str + File path to galaxy tool parameter + + infile_estimator : str, default is None + File path to estimator + + infile1 : str, default is None + File path to dataset containing features or true labels. + + infile2 : str, default is None + File path to dataset containing target values or predicted + probabilities. + + outfile_result : str, default is None + File path to save the results, either cv_results or test result + + outfile_object : str, default is None + File path to save searchCV object + + groups : str, default is None + File path to dataset containing groups labels + + ref_seq : str, default is None + File path to dataset containing genome sequence file + + intervals : str, default is None + File path to dataset containing interval file + + targets : str, default is None + File path to dataset compressed target bed file + + fasta_path : str, default is None + File path to dataset containing fasta file + + model_config : str, default is None + File path to dataset containing JSON config for neural networks + + true_labels : str, default is None + File path to dataset containing true labels + + predicted_labels : str, default is None + File path to dataset containing true predicted labels + + plot_color : str, default is None + Color of the confusion matrix heatmap + + title : str, default is None + Title of the confusion matrix heatmap + """ + warnings.simplefilter("ignore") + + with open(inputs, "r") as param_handler: + params = json.load(param_handler) + + title = params["plotting_selection"]["title"].strip() + plot_type = params["plotting_selection"]["plot_type"] + plot_format = params["plotting_selection"]["plot_format"] + + if plot_type == "feature_importances": + with open(infile_estimator, "rb") as estimator_handler: + estimator = load_model(estimator_handler) + + column_option = params["plotting_selection"]["column_selector_options"][ + "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["plotting_selection"]["column_selector_options"]["col1"] + else: + c = None + + _, input_df = read_columns( + infile1, + c=c, + c_option=column_option, + return_df=True, + sep="\t", + header="infer", + parse_dates=True, + ) + + feature_names = input_df.columns.values + + if isinstance(estimator, Pipeline): + for st in estimator.steps[:-1]: + if isinstance(st[-1], SelectorMixin): + mask = st[-1].get_support() + feature_names = feature_names[mask] + estimator = estimator.steps[-1][-1] + + if hasattr(estimator, "coef_"): + coefs = estimator.coef_ + else: + coefs = getattr(estimator, "feature_importances_", None) + if coefs is None: + raise RuntimeError( + "The classifier does not expose " + '"coef_" or "feature_importances_" ' + "attributes" + ) + + threshold = params["plotting_selection"]["threshold"] + if threshold is not None: + mask = (coefs > threshold) | (coefs < -threshold) + coefs = coefs[mask] + feature_names = feature_names[mask] + + # sort + indices = np.argsort(coefs)[::-1] + + trace = go.Bar(x=feature_names[indices], y=coefs[indices]) + layout = go.Layout(title=title or "Feature Importances") + fig = go.Figure(data=[trace], layout=layout) + + plotly.offline.plot(fig, filename="output.html", auto_open=False) + # to be discovered by `from_work_dir` + os.rename("output.html", "output") + + return 0 + + elif plot_type in ("pr_curve", "roc_curve"): + df1 = pd.read_csv(infile1, sep="\t", header="infer") + df2 = pd.read_csv(infile2, sep="\t", header="infer").astype(np.float32) + + minimum = params["plotting_selection"]["report_minimum_n_positives"] + # filter out columns whose n_positives is beblow the threhold + if minimum: + mask = df1.sum(axis=0) >= minimum + df1 = df1.loc[:, mask] + df2 = df2.loc[:, mask] + + pos_label = params["plotting_selection"]["pos_label"].strip() or None + + if plot_type == "pr_curve": + if plot_format == "plotly_html": + visualize_pr_curve_plotly(df1, df2, pos_label, title=title) + else: + visualize_pr_curve_matplotlib(df1, df2, pos_label, title) + else: # 'roc_curve' + drop_intermediate = params["plotting_selection"]["drop_intermediate"] + if plot_format == "plotly_html": + visualize_roc_curve_plotly( + df1, + df2, + pos_label, + drop_intermediate=drop_intermediate, + title=title, + ) + else: + visualize_roc_curve_matplotlib( + df1, + df2, + pos_label, + drop_intermediate=drop_intermediate, + title=title, + ) + + return 0 + + elif plot_type == "rfecv_gridscores": + input_df = pd.read_csv(infile1, sep="\t", header="infer") + scores = input_df.iloc[:, 0] + steps = params["plotting_selection"]["steps"].strip() + steps = safe_eval(steps) + + data = go.Scatter( + x=list(range(len(scores))), + y=scores, + text=[str(_) for _ in steps] if steps else None, + mode="lines", + ) + layout = go.Layout( + xaxis=dict(title="Number of features selected"), + yaxis=dict(title="Cross validation score"), + title=dict( + text=title or None, x=0.5, y=0.92, xanchor="center", yanchor="top" + ), + font=dict(family="sans-serif", size=11), + # control backgroud colors + plot_bgcolor="rgba(255,255,255,0)", + ) + """ + # legend=dict( + # x=0.95, + # y=0, + # traceorder="normal", + # font=dict( + # family="sans-serif", + # size=9, + # color="black" + # ), + # bgcolor="LightSteelBlue", + # bordercolor="Black", + # borderwidth=2 + # ), + """ + + fig = go.Figure(data=[data], layout=layout) + plotly.offline.plot(fig, filename="output.html", auto_open=False) + # to be discovered by `from_work_dir` + os.rename("output.html", "output") + + return 0 + + elif plot_type == "learning_curve": + input_df = pd.read_csv(infile1, sep="\t", header="infer") + plot_std_err = params["plotting_selection"]["plot_std_err"] + data1 = go.Scatter( + x=input_df["train_sizes_abs"], + y=input_df["mean_train_scores"], + error_y=dict(array=input_df["std_train_scores"]) if plot_std_err else None, + mode="lines", + name="Train Scores", + ) + data2 = go.Scatter( + x=input_df["train_sizes_abs"], + y=input_df["mean_test_scores"], + error_y=dict(array=input_df["std_test_scores"]) if plot_std_err else None, + mode="lines", + name="Test Scores", + ) + layout = dict( + xaxis=dict(title="No. of samples"), + yaxis=dict(title="Performance Score"), + # modify these configurations to customize image + title=dict( + text=title or "Learning Curve", + x=0.5, + y=0.92, + xanchor="center", + yanchor="top", + ), + font=dict(family="sans-serif", size=11), + # control backgroud colors + plot_bgcolor="rgba(255,255,255,0)", + ) + """ + # legend=dict( + # x=0.95, + # y=0, + # traceorder="normal", + # font=dict( + # family="sans-serif", + # size=9, + # color="black" + # ), + # bgcolor="LightSteelBlue", + # bordercolor="Black", + # borderwidth=2 + # ), + """ + + fig = go.Figure(data=[data1, data2], layout=layout) + plotly.offline.plot(fig, filename="output.html", auto_open=False) + # to be discovered by `from_work_dir` + os.rename("output.html", "output") + + return 0 + + elif plot_type == "keras_plot_model": + with open(model_config, "r") as f: + model_str = f.read() + model = model_from_json(model_str) + plot_model(model, to_file="output.png") + os.rename("output.png", "output") + + return 0 + + elif plot_type == "classification_confusion_matrix": + plot_selection = params["plotting_selection"] + input_true = get_dataframe( + true_labels, plot_selection, "header_true", "column_selector_options_true" + ) + header_predicted = "infer" if plot_selection["header_predicted"] else None + input_predicted = pd.read_csv( + predicted_labels, sep="\t", parse_dates=True, header=header_predicted + ) + true_classes = input_true.iloc[:, -1].copy() + predicted_classes = input_predicted.iloc[:, -1].copy() + axis_labels = list(set(true_classes)) + c_matrix = confusion_matrix(true_classes, predicted_classes) + fig, ax = plt.subplots(figsize=(7, 7)) + im = plt.imshow(c_matrix, cmap=plot_color) + for i in range(len(c_matrix)): + for j in range(len(c_matrix)): + ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k") + ax.set_ylabel("True class labels") + ax.set_xlabel("Predicted class labels") + ax.set_title(title) + ax.set_xticks(axis_labels) + ax.set_yticks(axis_labels) + fig.colorbar(im, ax=ax) + fig.tight_layout() + plt.savefig("output.png", dpi=125) + os.rename("output.png", "output") + + return 0 + + # save pdf file to disk + # fig.write_image("image.pdf", format='pdf') + # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2) + + +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("-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") + aparser.add_argument("-c", "--model_config", dest="model_config") + aparser.add_argument("-tl", "--true_labels", dest="true_labels") + aparser.add_argument("-pl", "--predicted_labels", dest="predicted_labels") + aparser.add_argument("-pc", "--plot_color", dest="plot_color") + aparser.add_argument("-pt", "--title", dest="title") + args = aparser.parse_args() + + main( + args.inputs, + args.infile_estimator, + args.infile1, + args.infile2, + args.outfile_result, + outfile_object=args.outfile_object, + groups=args.groups, + ref_seq=args.ref_seq, + intervals=args.intervals, + targets=args.targets, + fasta_path=args.fasta_path, + model_config=args.model_config, + true_labels=args.true_labels, + predicted_labels=args.predicted_labels, + plot_color=args.plot_color, + title=args.title, + ) diff -r 000000000000 -r 3b6ee54eb7e2 model_prediction.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/model_prediction.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,241 @@ +import argparse +import json +import warnings + +import numpy as np +import pandas as pd +from galaxy_ml.utils import get_module, load_model, read_columns, try_get_attr +from scipy.io import mmread +from sklearn.pipeline import Pipeline + +N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1)) + + +def main( + inputs, + infile_estimator, + outfile_predict, + infile_weights=None, + infile1=None, + fasta_path=None, + ref_seq=None, + vcf_path=None, +): + """ + Parameter + --------- + inputs : str + File path to galaxy tool parameter + + infile_estimator : strgit + File path to trained estimator input + + outfile_predict : str + File path to save the prediction results, tabular + + infile_weights : str + File path to weights input + + infile1 : str + File path to dataset containing features + + fasta_path : str + File path to dataset containing fasta file + + ref_seq : str + File path to dataset containing the reference genome sequence. + + vcf_path : str + File path to dataset containing variants info. + """ + warnings.filterwarnings("ignore") + + 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) + + 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 dataset for weights wan not selected!" + ) + main_est.load_weights(infile_weights) + + # handle data input + 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 = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True) + + X = read_columns(df, c=c, c_option=column_option).astype(float) + + if params["method"] == "predict": + preds = estimator.predict(X) + else: + preds = estimator.predict_proba(X) + + # sparse input + elif input_type == "sparse": + X = mmread(open(infile1, "r")) + if params["method"] == "predict": + preds = estimator.predict(X) + else: + preds = estimator.predict_proba(X) + + # fasta input + elif input_type == "seq_fasta": + if not hasattr(estimator, "data_batch_generator"): + raise ValueError( + "To do prediction on sequences in fasta input, " + "the estimator must be a `KerasGBatchClassifier`" + "equipped with data_batch_generator!" + ) + pyfaidx = get_module("pyfaidx") + sequences = pyfaidx.Fasta(fasta_path) + n_seqs = len(sequences.keys()) + X = np.arange(n_seqs)[:, np.newaxis] + seq_length = estimator.data_batch_generator.seq_length + batch_size = getattr(estimator, "batch_size", 32) + steps = (n_seqs + batch_size - 1) // batch_size + + seq_type = params["input_options"]["seq_type"] + klass = try_get_attr("galaxy_ml.preprocessors", seq_type) + + pred_data_generator = klass(fasta_path, seq_length=seq_length) + + if params["method"] == "predict": + preds = estimator.predict( + X, data_generator=pred_data_generator, steps=steps + ) + else: + preds = estimator.predict_proba( + X, data_generator=pred_data_generator, steps=steps + ) + + # vcf input + elif input_type == "variant_effect": + klass = try_get_attr("galaxy_ml.preprocessors", "GenomicVariantBatchGenerator") + + options = params["input_options"] + options.pop("selected_input") + if options["blacklist_regions"] == "none": + options["blacklist_regions"] = None + + pred_data_generator = klass( + ref_genome_path=ref_seq, vcf_path=vcf_path, **options + ) + + pred_data_generator.set_processing_attrs() + + variants = pred_data_generator.variants + + # predict 1600 sample at once then write to file + gen_flow = pred_data_generator.flow(batch_size=1600) + + file_writer = open(outfile_predict, "w") + header_row = "\t".join(["chrom", "pos", "name", "ref", "alt", "strand"]) + file_writer.write(header_row) + header_done = False + + steps_done = 0 + + # TODO: multiple threading + try: + while steps_done < len(gen_flow): + index_array = next(gen_flow.index_generator) + batch_X = gen_flow._get_batches_of_transformed_samples(index_array) + + if params["method"] == "predict": + batch_preds = estimator.predict( + batch_X, + # The presence of `pred_data_generator` below is to + # override model carrying data_generator if there + # is any. + data_generator=pred_data_generator, + ) + else: + batch_preds = estimator.predict_proba( + batch_X, + # The presence of `pred_data_generator` below is to + # override model carrying data_generator if there + # is any. + data_generator=pred_data_generator, + ) + + if batch_preds.ndim == 1: + batch_preds = batch_preds[:, np.newaxis] + + batch_meta = variants[index_array] + batch_out = np.column_stack([batch_meta, batch_preds]) + + if not header_done: + heads = np.arange(batch_preds.shape[-1]).astype(str) + heads_str = "\t".join(heads) + file_writer.write("\t%s\n" % heads_str) + header_done = True + + for row in batch_out: + row_str = "\t".join(row) + file_writer.write("%s\n" % row_str) + + steps_done += 1 + + finally: + file_writer.close() + # TODO: make api `pred_data_generator.close()` + pred_data_generator.close() + return 0 + # end input + + # output + if len(preds.shape) == 1: + rval = pd.DataFrame(preds, columns=["Predicted"]) + else: + rval = pd.DataFrame(preds) + + rval.to_csv(outfile_predict, 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("-O", "--outfile_predict", dest="outfile_predict") + aparser.add_argument("-f", "--fasta_path", dest="fasta_path") + aparser.add_argument("-r", "--ref_seq", dest="ref_seq") + aparser.add_argument("-v", "--vcf_path", dest="vcf_path") + args = aparser.parse_args() + + main( + args.inputs, + args.infile_estimator, + args.outfile_predict, + infile_weights=args.infile_weights, + infile1=args.infile1, + fasta_path=args.fasta_path, + ref_seq=args.ref_seq, + vcf_path=args.vcf_path, + ) diff -r 000000000000 -r 3b6ee54eb7e2 pca.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pca.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,186 @@ +import argparse + +import numpy as np +from galaxy_ml.utils import read_columns +from sklearn.decomposition import IncrementalPCA, KernelPCA, PCA + + +def main(): + parser = argparse.ArgumentParser(description="RDKit screen") + parser.add_argument("-i", "--infile", help="Input file") + parser.add_argument( + "--header", action="store_true", help="Include the header row or skip it" + ) + parser.add_argument( + "-c", + "--columns", + type=str.lower, + default="all", + choices=[ + "by_index_number", + "all_but_by_index_number", + "by_header_name", + "all_but_by_header_name", + "all_columns", + ], + help="Choose to select all columns, or exclude/include some", + ) + parser.add_argument( + "-ci", + "--column_indices", + type=str.lower, + help="Choose to select all columns, or exclude/include some", + ) + parser.add_argument( + "-n", + "--number", + nargs="?", + type=int, + default=None, + help="Number of components to keep. If not set, all components are kept", + ) + parser.add_argument("--whiten", action="store_true", help="Whiten the components") + parser.add_argument( + "-t", + "--pca_type", + type=str.lower, + default="classical", + choices=["classical", "incremental", "kernel"], + help="Choose which flavour of PCA to use", + ) + parser.add_argument( + "-s", + "--svd_solver", + type=str.lower, + default="auto", + choices=["auto", "full", "arpack", "randomized"], + help="Choose the type of svd solver.", + ) + parser.add_argument( + "-b", + "--batch_size", + nargs="?", + type=int, + default=None, + help="The number of samples to use for each batch", + ) + parser.add_argument( + "-k", + "--kernel", + type=str.lower, + default="linear", + choices=["linear", "poly", "rbf", "sigmoid", "cosine", "precomputed"], + help="Choose the type of kernel.", + ) + parser.add_argument( + "-g", + "--gamma", + nargs="?", + type=float, + default=None, + help="Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels", + ) + parser.add_argument( + "-tol", + "--tolerance", + type=float, + default=0.0, + help="Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack", + ) + parser.add_argument( + "-mi", + "--max_iter", + nargs="?", + type=int, + default=None, + help="Maximum number of iterations for arpack", + ) + parser.add_argument( + "-d", + "--degree", + type=int, + default=3, + help="Degree for poly kernels. Ignored by other kernels", + ) + parser.add_argument( + "-cf", + "--coef0", + type=float, + default=1.0, + help="Independent term in poly and sigmoid kernels", + ) + parser.add_argument( + "-e", + "--eigen_solver", + type=str.lower, + default="auto", + choices=["auto", "dense", "arpack"], + help="Choose the type of eigen solver.", + ) + parser.add_argument( + "-o", "--outfile", help="Base name for output file (no extension)." + ) + args = parser.parse_args() + + usecols = None + pca_params = {} + + if args.columns == "by_index_number" or args.columns == "all_but_by_index_number": + usecols = [int(i) for i in args.column_indices.split(",")] + elif args.columns == "by_header_name" or args.columns == "all_but_by_header_name": + usecols = args.column_indices + + header = "infer" if args.header else None + + pca_input = read_columns( + f=args.infile, + c=usecols, + c_option=args.columns, + sep="\t", + header=header, + parse_dates=True, + encoding=None, + index_col=None, + ) + + pca_params.update({"n_components": args.number}) + + if args.pca_type == "classical": + pca_params.update({"svd_solver": args.svd_solver, "whiten": args.whiten}) + if args.svd_solver == "arpack": + pca_params.update({"tol": args.tolerance}) + pca = PCA() + + elif args.pca_type == "incremental": + pca_params.update({"batch_size": args.batch_size, "whiten": args.whiten}) + pca = IncrementalPCA() + + elif args.pca_type == "kernel": + pca_params.update( + { + "kernel": args.kernel, + "eigen_solver": args.eigen_solver, + "gamma": args.gamma, + } + ) + + if args.kernel == "poly": + pca_params.update({"degree": args.degree, "coef0": args.coef0}) + elif args.kernel == "sigmoid": + pca_params.update({"coef0": args.coef0}) + elif args.kernel == "precomputed": + pca_input = np.dot(pca_input, pca_input.T) + + if args.eigen_solver == "arpack": + pca_params.update({"tol": args.tolerance, "max_iter": args.max_iter}) + + pca = KernelPCA() + + print(pca_params) + pca.set_params(**pca_params) + pca_output = pca.fit_transform(pca_input) + np.savetxt(fname=args.outfile, X=pca_output, fmt="%.4f", delimiter="\t") + + +if __name__ == "__main__": + main() diff -r 000000000000 -r 3b6ee54eb7e2 search_model_validation.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/search_model_validation.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,784 @@ +import argparse +import collections +import json +import os +import pickle +import sys +import warnings + +import imblearn +import joblib +import numpy as np +import pandas as pd +import skrebate +from galaxy_ml.utils import (clean_params, get_cv, + get_main_estimator, get_module, get_scoring, + load_model, read_columns, SafeEval, try_get_attr) +from scipy.io import mmread +from sklearn import (cluster, decomposition, feature_selection, + kernel_approximation, model_selection, preprocessing) +from sklearn.exceptions import FitFailedWarning +from sklearn.model_selection import _search, _validation +from sklearn.model_selection._validation import _score, cross_validate + +_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)) +# handle disk cache +CACHE_DIR = os.path.join(os.getcwd(), "cached") +del os +NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks") + + +def _eval_search_params(params_builder): + search_params = {} + + for p in params_builder["param_set"]: + search_list = p["sp_list"].strip() + if search_list == "": + continue + + param_name = p["sp_name"] + if param_name.lower().endswith(NON_SEARCHABLE): + print( + "Warning: `%s` is not eligible for search and was " + "omitted!" % param_name + ) + continue + + if not search_list.startswith(":"): + safe_eval = SafeEval(load_scipy=True, load_numpy=True) + ev = safe_eval(search_list) + search_params[param_name] = ev + else: + # Have `:` before search list, asks for estimator evaluatio + safe_eval_es = SafeEval(load_estimators=True) + search_list = search_list[1:].strip() + # TODO maybe add regular express check + ev = safe_eval_es(search_list) + preprocessings = ( + preprocessing.StandardScaler(), + preprocessing.Binarizer(), + preprocessing.MaxAbsScaler(), + preprocessing.Normalizer(), + preprocessing.MinMaxScaler(), + preprocessing.PolynomialFeatures(), + preprocessing.RobustScaler(), + feature_selection.SelectKBest(), + feature_selection.GenericUnivariateSelect(), + feature_selection.SelectPercentile(), + feature_selection.SelectFpr(), + feature_selection.SelectFdr(), + feature_selection.SelectFwe(), + feature_selection.VarianceThreshold(), + decomposition.FactorAnalysis(random_state=0), + decomposition.FastICA(random_state=0), + decomposition.IncrementalPCA(), + decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS), + decomposition.LatentDirichletAllocation(random_state=0, n_jobs=N_JOBS), + decomposition.MiniBatchDictionaryLearning( + random_state=0, n_jobs=N_JOBS + ), + decomposition.MiniBatchSparsePCA(random_state=0, n_jobs=N_JOBS), + decomposition.NMF(random_state=0), + decomposition.PCA(random_state=0), + decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS), + decomposition.TruncatedSVD(random_state=0), + kernel_approximation.Nystroem(random_state=0), + kernel_approximation.RBFSampler(random_state=0), + kernel_approximation.AdditiveChi2Sampler(), + kernel_approximation.SkewedChi2Sampler(random_state=0), + cluster.FeatureAgglomeration(), + skrebate.ReliefF(n_jobs=N_JOBS), + skrebate.SURF(n_jobs=N_JOBS), + skrebate.SURFstar(n_jobs=N_JOBS), + skrebate.MultiSURF(n_jobs=N_JOBS), + skrebate.MultiSURFstar(n_jobs=N_JOBS), + imblearn.under_sampling.ClusterCentroids(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.CondensedNearestNeighbour( + random_state=0, n_jobs=N_JOBS + ), + imblearn.under_sampling.EditedNearestNeighbours( + random_state=0, n_jobs=N_JOBS + ), + imblearn.under_sampling.RepeatedEditedNearestNeighbours( + random_state=0, n_jobs=N_JOBS + ), + imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.InstanceHardnessThreshold( + random_state=0, n_jobs=N_JOBS + ), + imblearn.under_sampling.NearMiss(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.NeighbourhoodCleaningRule( + random_state=0, n_jobs=N_JOBS + ), + imblearn.under_sampling.OneSidedSelection( + random_state=0, n_jobs=N_JOBS + ), + imblearn.under_sampling.RandomUnderSampler(random_state=0), + imblearn.under_sampling.TomekLinks(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.RandomOverSampler(random_state=0), + imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.BorderlineSMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.SMOTENC( + categorical_features=[], random_state=0, n_jobs=N_JOBS + ), + imblearn.combine.SMOTEENN(random_state=0), + imblearn.combine.SMOTETomek(random_state=0), + ) + newlist = [] + for obj in ev: + if obj is None: + newlist.append(None) + elif obj == "all_0": + newlist.extend(preprocessings[0:35]) + elif obj == "sk_prep_all": # no KernalCenter() + newlist.extend(preprocessings[0:7]) + elif obj == "fs_all": + newlist.extend(preprocessings[7:14]) + elif obj == "decomp_all": + newlist.extend(preprocessings[14:25]) + elif obj == "k_appr_all": + newlist.extend(preprocessings[25:29]) + elif obj == "reb_all": + newlist.extend(preprocessings[30:35]) + elif obj == "imb_all": + newlist.extend(preprocessings[35:54]) + elif type(obj) is int and -1 < obj < len(preprocessings): + newlist.append(preprocessings[obj]) + elif hasattr(obj, "get_params"): # user uploaded object + if "n_jobs" in obj.get_params(): + newlist.append(obj.set_params(n_jobs=N_JOBS)) + else: + newlist.append(obj) + else: + sys.exit("Unsupported estimator type: %r" % (obj)) + + search_params[param_name] = newlist + + return search_params + + +def _handle_X_y( + estimator, + params, + infile1, + infile2, + loaded_df={}, + ref_seq=None, + intervals=None, + targets=None, + fasta_path=None, +): + """read inputs + + Params + ------- + estimator : estimator object + params : dict + Galaxy tool parameter inputs + infile1 : str + File path to dataset containing features + infile2 : str + File path to dataset containing target values + loaded_df : dict + Contains loaded DataFrame objects with file path as keys + ref_seq : str + File path to dataset containing genome sequence file + interval : 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 + + + Returns + ------- + estimator : estimator object after setting new attributes + X : numpy array + y : numpy array + """ + estimator_params = estimator.get_params() + + 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) + + if df_key in loaded_df: + infile1 = loaded_df[df_key] + + 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 + + return estimator, X, y + + +def _do_outer_cv(searcher, X, y, outer_cv, scoring, error_score="raise", outfile=None): + """Do outer cross-validation for nested CV + + Parameters + ---------- + searcher : object + SearchCV object + X : numpy array + Containing features + y : numpy array + Target values or labels + outer_cv : int or CV splitter + Control the cv splitting + scoring : object + Scorer + error_score: str, float or numpy float + Whether to raise fit error or return an value + outfile : str + File path to store the restuls + """ + if error_score == "raise": + rval = cross_validate( + searcher, + X, + y, + scoring=scoring, + cv=outer_cv, + n_jobs=N_JOBS, + verbose=0, + error_score=error_score, + ) + else: + warnings.simplefilter("always", FitFailedWarning) + with warnings.catch_warnings(record=True) as w: + try: + rval = cross_validate( + searcher, + X, + y, + scoring=scoring, + cv=outer_cv, + n_jobs=N_JOBS, + verbose=0, + error_score=error_score, + ) + except ValueError: + pass + for warning in w: + print(repr(warning.message)) + + keys = list(rval.keys()) + for k in keys: + if k.startswith("test"): + rval["mean_" + k] = np.mean(rval[k]) + rval["std_" + k] = np.std(rval[k]) + if k.endswith("time"): + rval.pop(k) + rval = pd.DataFrame(rval) + rval = rval[sorted(rval.columns)] + rval.to_csv(path_or_buf=outfile, sep="\t", header=True, index=False) + + +def _do_train_test_split_val( + searcher, + X, + y, + params, + error_score="raise", + primary_scoring=None, + groups=None, + outfile=None, +): + """do train test split, searchCV validates on the train and then use + the best_estimator_ to evaluate on the test + + Returns + -------- + Fitted SearchCV object + """ + train_test_split = try_get_attr("galaxy_ml.model_validations", "train_test_split") + split_options = params["outer_split"] + + # splits + if split_options["shuffle"] == "stratified": + split_options["labels"] = y + X, X_test, y, y_test = train_test_split(X, y, **split_options) + elif split_options["shuffle"] == "group": + if groups is None: + raise ValueError( + "No group based CV option was choosen for " "group shuffle!" + ) + split_options["labels"] = groups + if y is None: + X, X_test, groups, _ = train_test_split(X, groups, **split_options) + else: + X, X_test, y, y_test, groups, _ = train_test_split( + X, y, groups, **split_options + ) + else: + if split_options["shuffle"] == "None": + split_options["shuffle"] = None + X, X_test, y, y_test = train_test_split(X, y, **split_options) + + if error_score == "raise": + searcher.fit(X, y, groups=groups) + else: + warnings.simplefilter("always", FitFailedWarning) + with warnings.catch_warnings(record=True) as w: + try: + searcher.fit(X, y, groups=groups) + except ValueError: + pass + for warning in w: + print(repr(warning.message)) + + scorer_ = searcher.scorer_ + if isinstance(scorer_, collections.Mapping): + is_multimetric = True + else: + is_multimetric = False + + best_estimator_ = getattr(searcher, "best_estimator_") + + # TODO Solve deep learning models in pipeline + if best_estimator_.__class__.__name__ == "KerasGBatchClassifier": + test_score = best_estimator_.evaluate( + X_test, scorer=scorer_, is_multimetric=is_multimetric + ) + else: + test_score = _score( + best_estimator_, X_test, y_test, scorer_, is_multimetric=is_multimetric + ) + + if not is_multimetric: + test_score = {primary_scoring: test_score} + for key, value in test_score.items(): + test_score[key] = [value] + result_df = pd.DataFrame(test_score) + result_df.to_csv(path_or_buf=outfile, sep="\t", header=True, index=False) + + return searcher + + +def main( + inputs, + infile_estimator, + infile1, + infile2, + outfile_result, + outfile_object=None, + outfile_weights=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 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") + + # store read dataframe object + loaded_df = {} + + with open(inputs, "r") as param_handler: + params = json.load(param_handler) + + # Override the refit parameter + params["search_schemes"]["options"]["refit"] = ( + True if params["save"] != "nope" else False + ) + + with open(infile_estimator, "rb") as estimator_handler: + estimator = load_model(estimator_handler) + + optimizer = params["search_schemes"]["selected_search_scheme"] + optimizer = getattr(model_selection, optimizer) + + # handle gridsearchcv options + options = params["search_schemes"]["options"] + + if groups: + header = ( + "infer" if (options["cv_selector"]["groups_selector"]["header_g"]) else None + ) + column_option = options["cv_selector"]["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 = options["cv_selector"]["groups_selector"]["column_selector_options_g"][ + "col_g" + ] + else: + c = None + + df_key = groups + repr(header) + + groups = pd.read_csv(groups, sep="\t", header=header, parse_dates=True) + loaded_df[df_key] = groups + + groups = read_columns( + groups, + c=c, + c_option=column_option, + sep="\t", + header=header, + parse_dates=True, + ) + groups = groups.ravel() + options["cv_selector"]["groups_selector"] = groups + + splitter, groups = get_cv(options.pop("cv_selector")) + options["cv"] = splitter + primary_scoring = options["scoring"]["primary_scoring"] + # get_scoring() expects secondary_scoring to be a comma separated string (not a list) + # Check if secondary_scoring is specified + secondary_scoring = options["scoring"].get("secondary_scoring", None) + if secondary_scoring is not None: + # If secondary_scoring is specified, convert the list into comman separated string + options["scoring"]["secondary_scoring"] = ",".join( + options["scoring"]["secondary_scoring"] + ) + options["scoring"] = get_scoring(options["scoring"]) + if options["error_score"]: + options["error_score"] = "raise" + else: + options["error_score"] = np.nan + if options["refit"] and isinstance(options["scoring"], dict): + options["refit"] = primary_scoring + if "pre_dispatch" in options and options["pre_dispatch"] == "": + options["pre_dispatch"] = None + + params_builder = params["search_schemes"]["search_params_builder"] + param_grid = _eval_search_params(params_builder) + + estimator = clean_params(estimator) + + # save the SearchCV object without fit + if params["save"] == "save_no_fit": + searcher = optimizer(estimator, param_grid, **options) + print(searcher) + with open(outfile_object, "wb") as output_handler: + pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL) + return 0 + + # read inputs and loads new attributes, like paths + estimator, X, y = _handle_X_y( + estimator, + params, + infile1, + infile2, + loaded_df=loaded_df, + ref_seq=ref_seq, + intervals=intervals, + targets=targets, + fasta_path=fasta_path, + ) + + # 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) + + searcher = optimizer(estimator, param_grid, **options) + + split_mode = params["outer_split"].pop("split_mode") + + if split_mode == "nested_cv": + # make sure refit is choosen + # this could be True for sklearn models, but not the case for + # deep learning models + if not options["refit"] and not all( + hasattr(estimator, attr) for attr in ("config", "model_type") + ): + warnings.warn("Refit is change to `True` for nested validation!") + setattr(searcher, "refit", True) + + outer_cv, _ = get_cv(params["outer_split"]["cv_selector"]) + # nested CV, outer cv using cross_validate + if options["error_score"] == "raise": + rval = cross_validate( + searcher, + X, + y, + scoring=options["scoring"], + cv=outer_cv, + n_jobs=N_JOBS, + verbose=options["verbose"], + return_estimator=(params["save"] == "save_estimator"), + error_score=options["error_score"], + return_train_score=True, + ) + else: + warnings.simplefilter("always", FitFailedWarning) + with warnings.catch_warnings(record=True) as w: + try: + rval = cross_validate( + searcher, + X, + y, + scoring=options["scoring"], + cv=outer_cv, + n_jobs=N_JOBS, + verbose=options["verbose"], + return_estimator=(params["save"] == "save_estimator"), + error_score=options["error_score"], + return_train_score=True, + ) + except ValueError: + pass + for warning in w: + print(repr(warning.message)) + + fitted_searchers = rval.pop("estimator", []) + if fitted_searchers: + import os + + pwd = os.getcwd() + save_dir = os.path.join(pwd, "cv_results_in_folds") + try: + os.mkdir(save_dir) + for idx, obj in enumerate(fitted_searchers): + target_name = "cv_results_" + "_" + "split%d" % idx + target_path = os.path.join(pwd, save_dir, target_name) + cv_results_ = getattr(obj, "cv_results_", None) + if not cv_results_: + print("%s is not available" % target_name) + continue + cv_results_ = pd.DataFrame(cv_results_) + cv_results_ = cv_results_[sorted(cv_results_.columns)] + cv_results_.to_csv(target_path, sep="\t", header=True, index=False) + except Exception as e: + print(e) + finally: + del os + + keys = list(rval.keys()) + for k in keys: + if k.startswith("test"): + rval["mean_" + k] = np.mean(rval[k]) + rval["std_" + k] = np.std(rval[k]) + if k.endswith("time"): + rval.pop(k) + rval = pd.DataFrame(rval) + rval = rval[sorted(rval.columns)] + rval.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False) + # deprecate train test split mode + """searcher = _do_train_test_split_val( + searcher, X, y, params, + primary_scoring=primary_scoring, + error_score=options['error_score'], + groups=groups, + outfile=outfile_result)""" + return 0 + + # no outer split + else: + searcher.set_params(n_jobs=N_JOBS) + if options["error_score"] == "raise": + searcher.fit(X, y, groups=groups) + else: + warnings.simplefilter("always", FitFailedWarning) + with warnings.catch_warnings(record=True) as w: + try: + searcher.fit(X, y, groups=groups) + except ValueError: + pass + for warning in w: + print(repr(warning.message)) + + cv_results = pd.DataFrame(searcher.cv_results_) + cv_results = cv_results[sorted(cv_results.columns)] + cv_results.to_csv( + path_or_buf=outfile_result, sep="\t", header=True, index=False + ) + + memory.clear(warn=False) + + # output best estimator, and weights if applicable + if outfile_object: + best_estimator_ = getattr(searcher, "best_estimator_", None) + if not best_estimator_: + warnings.warn( + "GridSearchCV object has no attribute " + "'best_estimator_', because either it's " + "nested gridsearch or `refit` is False!" + ) + return + + # clean prams + best_estimator_ = clean_params(best_estimator_) + + main_est = get_main_estimator(best_estimator_) + + 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_ + 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: + print("Best estimator is saved: %s " % repr(best_estimator_)) + pickle.dump(best_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("-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, + groups=args.groups, + ref_seq=args.ref_seq, + intervals=args.intervals, + targets=args.targets, + fasta_path=args.fasta_path, + ) diff -r 000000000000 -r 3b6ee54eb7e2 simple_model_fit.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/simple_model_fit.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,194 @@ +import argparse +import json +import pickle + +import pandas as pd +from galaxy_ml.utils import load_model, read_columns +from scipy.io import mmread +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_ + 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(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, + ) diff -r 000000000000 -r 3b6ee54eb7e2 stacking_ensembles.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/stacking_ensembles.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,131 @@ +import argparse +import ast +import json +import pickle +import sys +import warnings + +import mlxtend.classifier +import mlxtend.regressor +import pandas as pd +from galaxy_ml.utils import (get_cv, get_estimator, get_search_params, + load_model) + +warnings.filterwarnings("ignore") + +N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1)) + + +def main(inputs_path, output_obj, base_paths=None, meta_path=None, outfile_params=None): + """ + Parameter + --------- + inputs_path : str + File path for Galaxy parameters + + output_obj : str + File path for ensemble estimator ouput + + base_paths : str + File path or paths concatenated by comma. + + meta_path : str + File path + + outfile_params : str + File path for params output + """ + with open(inputs_path, "r") as param_handler: + params = json.load(param_handler) + + estimator_type = params["algo_selection"]["estimator_type"] + # get base estimators + base_estimators = [] + for idx, base_file in enumerate(base_paths.split(",")): + if base_file and base_file != "None": + with open(base_file, "rb") as handler: + model = load_model(handler) + else: + estimator_json = params["base_est_builder"][idx]["estimator_selector"] + model = get_estimator(estimator_json) + + if estimator_type.startswith("sklearn"): + named = model.__class__.__name__.lower() + named = "base_%d_%s" % (idx, named) + base_estimators.append((named, model)) + else: + base_estimators.append(model) + + # get meta estimator, if applicable + if estimator_type.startswith("mlxtend"): + if meta_path: + with open(meta_path, "rb") as f: + meta_estimator = load_model(f) + else: + estimator_json = params["algo_selection"]["meta_estimator"][ + "estimator_selector" + ] + meta_estimator = get_estimator(estimator_json) + + options = params["algo_selection"]["options"] + + cv_selector = options.pop("cv_selector", None) + if cv_selector: + splitter, _groups = get_cv(cv_selector) + options["cv"] = splitter + # set n_jobs + options["n_jobs"] = N_JOBS + + weights = options.pop("weights", None) + if weights: + weights = ast.literal_eval(weights) + if weights: + options["weights"] = weights + + mod_and_name = estimator_type.split("_") + mod = sys.modules[mod_and_name[0]] + klass = getattr(mod, mod_and_name[1]) + + if estimator_type.startswith("sklearn"): + options["n_jobs"] = N_JOBS + ensemble_estimator = klass(base_estimators, **options) + + elif mod == mlxtend.classifier: + ensemble_estimator = klass( + classifiers=base_estimators, meta_classifier=meta_estimator, **options + ) + + else: + ensemble_estimator = klass( + regressors=base_estimators, meta_regressor=meta_estimator, **options + ) + + print(ensemble_estimator) + for base_est in base_estimators: + print(base_est) + + with open(output_obj, "wb") as out_handler: + pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL) + + if params["get_params"] and outfile_params: + results = get_search_params(ensemble_estimator) + df = pd.DataFrame(results, columns=["", "Parameter", "Value"]) + df.to_csv(outfile_params, sep="\t", index=False) + + +if __name__ == "__main__": + aparser = argparse.ArgumentParser() + aparser.add_argument("-b", "--bases", dest="bases") + aparser.add_argument("-m", "--meta", dest="meta") + aparser.add_argument("-i", "--inputs", dest="inputs") + aparser.add_argument("-o", "--outfile", dest="outfile") + aparser.add_argument("-p", "--outfile_params", dest="outfile_params") + args = aparser.parse_args() + + main( + args.inputs, + args.outfile, + base_paths=args.bases, + meta_path=args.meta, + outfile_params=args.outfile_params, + ) diff -r 000000000000 -r 3b6ee54eb7e2 test-data/GridSearchCV.zip Binary file test-data/GridSearchCV.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/LinearRegression01.zip Binary file test-data/LinearRegression01.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/LinearRegression02.zip Binary file test-data/LinearRegression02.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/RF01704.fasta --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/RF01704.fasta Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,4 @@ +>CP000097.1/1411351-1411410 +CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA +>ACNY01000002.1/278641-278580 +GAUCGUUCACUUCGCAUCGCGCGAAGCGCAGUUCGCCUCAGGCCAUGGAACGGGGACCUGAG diff -r 000000000000 -r 3b6ee54eb7e2 test-data/RFE.zip Binary file test-data/RFE.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/RandomForestClassifier.zip Binary file test-data/RandomForestClassifier.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/RandomForestRegressor01.zip Binary file test-data/RandomForestRegressor01.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/StackingCVRegressor01.zip Binary file test-data/StackingCVRegressor01.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/StackingCVRegressor02.zip Binary file test-data/StackingCVRegressor02.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/StackingRegressor02.zip Binary file test-data/StackingRegressor02.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/StackingVoting03.zip Binary file test-data/StackingVoting03.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/XGBRegressor01.zip Binary file test-data/XGBRegressor01.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/abc_model01 Binary file test-data/abc_model01 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/abc_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/abc_result01 Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,6 @@ +0 1 2 3 predicted +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/abr_model01 Binary file test-data/abr_model01 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/abr_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/abr_result01 Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,6 @@ +0 1 2 3 4 predicted +86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.323842059244 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 1.1503117056799999 +-47.4101632272 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test-data/best_estimator_.zip Binary file test-data/best_estimator_.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/best_params_.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/best_params_.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,1 @@ +{'estimator__n_estimators': 100} \ No newline at end of file diff -r 000000000000 -r 3b6ee54eb7e2 test-data/best_score_.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/best_score_.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2 @@ +best_score_ +0.7976348550293088 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/blobs.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,101 @@ +0 1 0 +0.33681845896740 -3.40287961299073 0 +-9.48324265575857 -8.66266051536995 2 +-1.93336328496076 5.70953908146890 1 +-10.03182405989413 -5.57834393458362 2 +0.54186077661701 -4.37693628326930 0 +-8.12962929067378 -7.05554320549807 2 +-0.73082578569427 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0 0 0 1 0 0 0 59.66542877819202 +2016 4 6 60 57 56.8 53 59 57 64 0 0 0 0 0 0 1 59.301283011436794 +2016 7 2 73 76 73.3 70 77 73 84 0 0 1 0 0 0 0 71.22373270826222 +2016 7 5 71 68 74.0 72 77 74 62 0 0 0 0 0 1 0 69.18347305115272 +2016 7 19 80 73 76.6 76 78 77 90 0 0 0 0 0 1 0 77.46150755171419 +2016 12 9 40 41 46.0 43 51 44 54 1 0 0 0 0 0 0 41.72540550328788 +2016 6 29 85 79 72.6 68 76 74 81 0 0 0 0 0 0 1 76.10594345672801 +2016 3 22 55 56 54.6 51 55 54 64 0 0 0 0 0 1 0 58.39058086785531 +2016 4 3 71 63 56.3 54 61 56 64 0 0 0 1 0 0 0 60.14340322699943 +2016 1 17 48 54 47.4 45 51 46 47 0 0 0 1 0 0 0 50.26292708961779 +2016 3 10 54 55 52.8 49 55 53 50 0 0 0 0 1 0 0 55.522605642512985 +2016 5 9 82 63 63.4 59 66 62 64 0 1 0 0 0 0 0 61.00788720614107 +2016 1 8 51 45 46.3 43 47 46 34 1 0 0 0 0 0 0 44.83434926564482 +2016 8 11 72 76 76.9 74 81 75 80 0 0 0 0 1 0 0 74.70250254902773 +2016 12 29 47 48 45.3 43 50 45 65 0 0 0 0 1 0 0 49.53438043623214 +2016 11 23 54 54 49.1 48 52 49 38 0 0 0 0 0 0 1 51.467278500089826 +2016 11 19 52 55 50.0 50 54 49 56 0 0 1 0 0 0 0 53.781953941654095 +2016 4 7 57 68 56.9 52 61 55 38 0 0 0 0 1 0 0 68.59176558339176 +2016 6 4 71 80 67.9 63 72 66 76 0 0 1 0 0 0 0 72.73805569547436 +2016 6 17 67 71 70.0 66 74 69 54 1 0 0 0 0 0 0 74.00873400230815 +2016 10 5 61 63 63.7 61 66 65 48 0 0 0 0 0 0 1 63.553834877849695 +2016 3 4 55 59 51.9 47 56 53 45 1 0 0 0 0 0 0 57.389419897063036 +2016 12 22 51 49 45.1 42 47 46 38 0 0 0 0 1 0 0 44.218563783534144 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,6 @@ + Parameter Value +@ copy_X copy_X: True +@ fit_intercept fit_intercept: True +* n_jobs n_jobs: 1 +@ normalize normalize: False + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params01.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,30 @@ + Parameter Value +* memory memory: None +* steps "steps: [('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)), ('selectkbest', SelectKBest(k=10, score_func=)), ('svr', SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, + gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True, + tol=0.001, verbose=False))]" +@ robustscaler "robustscaler: RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)" +@ selectkbest selectkbest: SelectKBest(k=10, score_func=) +@ svr "svr: SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, + gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True, + tol=0.001, verbose=False)" +@ robustscaler__copy robustscaler__copy: True +@ robustscaler__quantile_range robustscaler__quantile_range: (25.0, 75.0) +@ robustscaler__with_centering robustscaler__with_centering: True +@ robustscaler__with_scaling robustscaler__with_scaling: True +@ selectkbest__k selectkbest__k: 10 +@ selectkbest__score_func selectkbest__score_func: +@ svr__C svr__C: 1.0 +@ svr__cache_size svr__cache_size: 200 +@ svr__coef0 svr__coef0: 0.0 +@ svr__degree svr__degree: 3 +@ svr__epsilon svr__epsilon: 0.1 +@ svr__gamma svr__gamma: 'auto_deprecated' +@ svr__kernel svr__kernel: 'linear' +@ svr__max_iter svr__max_iter: -1 +@ svr__shrinking svr__shrinking: True +@ svr__tol svr__tol: 0.001 +* svr__verbose svr__verbose: False + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params02.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,33 @@ + Parameter Value +* memory memory: None +* steps "steps: [('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)), ('lassocv', LassoCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True, + max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False, + precompute='auto', random_state=None, selection='cyclic', tol=0.0001, + verbose=False))]" +@ robustscaler "robustscaler: RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)" +@ lassocv "lassocv: LassoCV(alphas=None, copy_X=True, cv='warn', eps=0.001, fit_intercept=True, + max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False, + precompute='auto', random_state=None, selection='cyclic', tol=0.0001, + verbose=False)" +@ robustscaler__copy robustscaler__copy: True +@ robustscaler__quantile_range robustscaler__quantile_range: (25.0, 75.0) +@ robustscaler__with_centering robustscaler__with_centering: True +@ robustscaler__with_scaling robustscaler__with_scaling: True +@ lassocv__alphas lassocv__alphas: None +@ lassocv__copy_X lassocv__copy_X: True +@ lassocv__cv lassocv__cv: 'warn' +@ lassocv__eps lassocv__eps: 0.001 +@ lassocv__fit_intercept lassocv__fit_intercept: True +@ lassocv__max_iter lassocv__max_iter: 1000 +@ lassocv__n_alphas lassocv__n_alphas: 100 +* lassocv__n_jobs lassocv__n_jobs: 1 +@ lassocv__normalize lassocv__normalize: False +@ lassocv__positive lassocv__positive: False +@ lassocv__precompute lassocv__precompute: 'auto' +@ lassocv__random_state lassocv__random_state: None +@ lassocv__selection lassocv__selection: 'cyclic' +@ lassocv__tol lassocv__tol: 0.0001 +* lassocv__verbose lassocv__verbose: False + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params03.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params03.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,43 @@ + Parameter Value +* memory memory: None +* steps "steps: [('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)), ('xgbclassifier', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='binary:logistic', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1))]" +@ robustscaler "robustscaler: RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True, + with_scaling=True)" +@ xgbclassifier "xgbclassifier: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='binary:logistic', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1)" +@ robustscaler__copy robustscaler__copy: True +@ robustscaler__quantile_range robustscaler__quantile_range: (25.0, 75.0) +@ robustscaler__with_centering robustscaler__with_centering: True +@ robustscaler__with_scaling robustscaler__with_scaling: True +@ xgbclassifier__base_score xgbclassifier__base_score: 0.5 +@ xgbclassifier__booster xgbclassifier__booster: 'gbtree' +@ xgbclassifier__colsample_bylevel xgbclassifier__colsample_bylevel: 1 +@ xgbclassifier__colsample_bytree xgbclassifier__colsample_bytree: 1 +@ xgbclassifier__gamma xgbclassifier__gamma: 0 +@ xgbclassifier__learning_rate xgbclassifier__learning_rate: 0.1 +@ xgbclassifier__max_delta_step xgbclassifier__max_delta_step: 0 +@ xgbclassifier__max_depth xgbclassifier__max_depth: 3 +@ xgbclassifier__min_child_weight xgbclassifier__min_child_weight: 1 +@ xgbclassifier__missing xgbclassifier__missing: nan +@ xgbclassifier__n_estimators xgbclassifier__n_estimators: 100 +* xgbclassifier__n_jobs xgbclassifier__n_jobs: 1 +* xgbclassifier__nthread xgbclassifier__nthread: None +@ xgbclassifier__objective xgbclassifier__objective: 'binary:logistic' +@ xgbclassifier__random_state xgbclassifier__random_state: 0 +@ xgbclassifier__reg_alpha xgbclassifier__reg_alpha: 0 +@ xgbclassifier__reg_lambda xgbclassifier__reg_lambda: 1 +@ xgbclassifier__scale_pos_weight xgbclassifier__scale_pos_weight: 1 +@ xgbclassifier__seed xgbclassifier__seed: None +@ xgbclassifier__silent xgbclassifier__silent: True +@ xgbclassifier__subsample xgbclassifier__subsample: 1 + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params04.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params04.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,39 @@ + Parameter Value +* memory memory: None +* steps "steps: [('selectfrommodel', SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None), + max_features=None, norm_order=1, prefit=False, threshold=None)), ('linearsvc', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, + intercept_scaling=1, loss='squared_hinge', max_iter=1000, + multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, + verbose=0))]" +@ selectfrommodel "selectfrommodel: SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None), + max_features=None, norm_order=1, prefit=False, threshold=None)" +@ linearsvc "linearsvc: LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, + intercept_scaling=1, loss='squared_hinge', max_iter=1000, + multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, + verbose=0)" +@ selectfrommodel__estimator__algorithm selectfrommodel__estimator__algorithm: 'SAMME.R' +@ selectfrommodel__estimator__base_estimator selectfrommodel__estimator__base_estimator: None +@ selectfrommodel__estimator__learning_rate selectfrommodel__estimator__learning_rate: 1.0 +@ selectfrommodel__estimator__n_estimators selectfrommodel__estimator__n_estimators: 50 +@ selectfrommodel__estimator__random_state selectfrommodel__estimator__random_state: None +@ selectfrommodel__estimator "selectfrommodel__estimator: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None)" +@ selectfrommodel__max_features selectfrommodel__max_features: None +@ selectfrommodel__norm_order selectfrommodel__norm_order: 1 +@ selectfrommodel__prefit selectfrommodel__prefit: False +@ selectfrommodel__threshold selectfrommodel__threshold: None +@ linearsvc__C linearsvc__C: 1.0 +@ linearsvc__class_weight linearsvc__class_weight: None +@ linearsvc__dual linearsvc__dual: True +@ linearsvc__fit_intercept linearsvc__fit_intercept: True +@ linearsvc__intercept_scaling linearsvc__intercept_scaling: 1 +@ linearsvc__loss linearsvc__loss: 'squared_hinge' +@ linearsvc__max_iter linearsvc__max_iter: 1000 +@ linearsvc__multi_class linearsvc__multi_class: 'ovr' +@ linearsvc__penalty linearsvc__penalty: 'l2' +@ linearsvc__random_state linearsvc__random_state: None +@ linearsvc__tol linearsvc__tol: 0.0001 +* linearsvc__verbose linearsvc__verbose: 0 + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params05.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params05.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,18 @@ + Parameter Value +@ bootstrap bootstrap: True +@ criterion criterion: 'mse' +@ max_depth max_depth: None +@ max_features max_features: 'auto' +@ max_leaf_nodes max_leaf_nodes: None +@ min_impurity_decrease min_impurity_decrease: 0.0 +@ min_impurity_split min_impurity_split: None +@ min_samples_leaf min_samples_leaf: 1 +@ min_samples_split min_samples_split: 2 +@ min_weight_fraction_leaf min_weight_fraction_leaf: 0.0 +@ n_estimators n_estimators: 100 +* n_jobs n_jobs: 1 +@ oob_score oob_score: False +@ random_state random_state: 42 +* verbose verbose: 0 +@ warm_start warm_start: False + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params06.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params06.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,22 @@ + Parameter Value +* memory memory: None +* steps "steps: [('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None, + svd_solver='auto', tol=0.0, whiten=False)), ('adaboostregressor', AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None))]" +@ pca "pca: PCA(copy=True, iterated_power='auto', n_components=None, random_state=None, + svd_solver='auto', tol=0.0, whiten=False)" +@ adaboostregressor "adaboostregressor: AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None)" +@ pca__copy pca__copy: True +@ pca__iterated_power pca__iterated_power: 'auto' +@ pca__n_components pca__n_components: None +@ pca__random_state pca__random_state: None +@ pca__svd_solver pca__svd_solver: 'auto' +@ pca__tol pca__tol: 0.0 +@ pca__whiten pca__whiten: False +@ adaboostregressor__base_estimator adaboostregressor__base_estimator: None +@ adaboostregressor__learning_rate adaboostregressor__learning_rate: 1.0 +@ adaboostregressor__loss adaboostregressor__loss: 'linear' +@ adaboostregressor__n_estimators adaboostregressor__n_estimators: 50 +@ adaboostregressor__random_state adaboostregressor__random_state: None + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params07.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params07.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,16 @@ + Parameter Value +* memory memory: None +* steps "steps: [('rbfsampler', RBFSampler(gamma=2.0, n_components=10, random_state=None)), ('adaboostclassifier', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None))]" +@ rbfsampler rbfsampler: RBFSampler(gamma=2.0, n_components=10, random_state=None) +@ adaboostclassifier "adaboostclassifier: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None)" +@ rbfsampler__gamma rbfsampler__gamma: 2.0 +@ rbfsampler__n_components rbfsampler__n_components: 10 +@ rbfsampler__random_state rbfsampler__random_state: None +@ adaboostclassifier__algorithm adaboostclassifier__algorithm: 'SAMME.R' +@ adaboostclassifier__base_estimator adaboostclassifier__base_estimator: None +@ adaboostclassifier__learning_rate adaboostclassifier__learning_rate: 1.0 +@ adaboostclassifier__n_estimators adaboostclassifier__n_estimators: 50 +@ adaboostclassifier__random_state adaboostclassifier__random_state: None + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params08.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params08.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,24 @@ + Parameter Value +* memory memory: None +* steps "steps: [('featureagglomeration', FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto', + connectivity=None, linkage='ward', memory=None, n_clusters=3, + pooling_func=)), ('adaboostclassifier', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None))]" +@ featureagglomeration "featureagglomeration: FeatureAgglomeration(affinity='euclidean', compute_full_tree='auto', + connectivity=None, linkage='ward', memory=None, n_clusters=3, + pooling_func=)" +@ adaboostclassifier "adaboostclassifier: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, + learning_rate=1.0, n_estimators=50, random_state=None)" +@ featureagglomeration__affinity featureagglomeration__affinity: 'euclidean' +@ featureagglomeration__compute_full_tree featureagglomeration__compute_full_tree: 'auto' +@ featureagglomeration__connectivity featureagglomeration__connectivity: None +@ featureagglomeration__linkage featureagglomeration__linkage: 'ward' +* featureagglomeration__memory featureagglomeration__memory: None +@ featureagglomeration__n_clusters featureagglomeration__n_clusters: 3 +@ featureagglomeration__pooling_func featureagglomeration__pooling_func: +@ adaboostclassifier__algorithm adaboostclassifier__algorithm: 'SAMME.R' +@ adaboostclassifier__base_estimator adaboostclassifier__base_estimator: None +@ adaboostclassifier__learning_rate adaboostclassifier__learning_rate: 1.0 +@ adaboostclassifier__n_estimators adaboostclassifier__n_estimators: 50 +@ adaboostclassifier__random_state adaboostclassifier__random_state: None + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params09.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params09.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,39 @@ + Parameter Value +* memory memory: None +* steps "steps: [('relieff', ReliefF(discrete_threshold=10, n_features_to_select=3, n_jobs=1, + n_neighbors=100, verbose=False)), ('randomforestregressor', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, + max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, warm_start=False))]" +@ relieff "relieff: ReliefF(discrete_threshold=10, n_features_to_select=3, n_jobs=1, + n_neighbors=100, verbose=False)" +@ randomforestregressor "randomforestregressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, + max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, warm_start=False)" +@ relieff__discrete_threshold relieff__discrete_threshold: 10 +@ relieff__n_features_to_select relieff__n_features_to_select: 3 +* relieff__n_jobs relieff__n_jobs: 1 +@ relieff__n_neighbors relieff__n_neighbors: 100 +* relieff__verbose relieff__verbose: False +@ randomforestregressor__bootstrap randomforestregressor__bootstrap: True +@ randomforestregressor__criterion randomforestregressor__criterion: 'mse' +@ randomforestregressor__max_depth randomforestregressor__max_depth: None +@ randomforestregressor__max_features randomforestregressor__max_features: 'auto' +@ randomforestregressor__max_leaf_nodes randomforestregressor__max_leaf_nodes: None +@ randomforestregressor__min_impurity_decrease randomforestregressor__min_impurity_decrease: 0.0 +@ randomforestregressor__min_impurity_split randomforestregressor__min_impurity_split: None +@ randomforestregressor__min_samples_leaf randomforestregressor__min_samples_leaf: 1 +@ randomforestregressor__min_samples_split randomforestregressor__min_samples_split: 2 +@ randomforestregressor__min_weight_fraction_leaf randomforestregressor__min_weight_fraction_leaf: 0.0 +@ randomforestregressor__n_estimators randomforestregressor__n_estimators: 'warn' +* randomforestregressor__n_jobs randomforestregressor__n_jobs: 1 +@ randomforestregressor__oob_score randomforestregressor__oob_score: False +@ randomforestregressor__random_state randomforestregressor__random_state: None +* randomforestregressor__verbose randomforestregressor__verbose: 0 +@ randomforestregressor__warm_start randomforestregressor__warm_start: False + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params10.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params10.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,12 @@ + Parameter Value +* memory memory: None +* steps "steps: [('adaboostregressor', AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None))]" +@ adaboostregressor "adaboostregressor: AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', + n_estimators=50, random_state=None)" +@ adaboostregressor__base_estimator adaboostregressor__base_estimator: None +@ adaboostregressor__learning_rate adaboostregressor__learning_rate: 1.0 +@ adaboostregressor__loss adaboostregressor__loss: 'linear' +@ adaboostregressor__n_estimators adaboostregressor__n_estimators: 50 +@ adaboostregressor__random_state adaboostregressor__random_state: None + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params11.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params11.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,46 @@ + Parameter Value +* memory memory: None +* steps "steps: [('editednearestneighbours', EditedNearestNeighbours(kind_sel='all', n_jobs=1, n_neighbors=3, + random_state=None, ratio=None, return_indices=False, + sampling_strategy='auto')), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', + max_depth=None, max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, + warm_start=False))]" +@ editednearestneighbours "editednearestneighbours: EditedNearestNeighbours(kind_sel='all', n_jobs=1, n_neighbors=3, + random_state=None, ratio=None, return_indices=False, + sampling_strategy='auto')" +@ randomforestclassifier "randomforestclassifier: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', + max_depth=None, max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, + oob_score=False, random_state=None, verbose=0, + warm_start=False)" +@ editednearestneighbours__kind_sel editednearestneighbours__kind_sel: 'all' +* editednearestneighbours__n_jobs editednearestneighbours__n_jobs: 1 +@ editednearestneighbours__n_neighbors editednearestneighbours__n_neighbors: 3 +@ editednearestneighbours__random_state editednearestneighbours__random_state: None +@ editednearestneighbours__ratio editednearestneighbours__ratio: None +@ editednearestneighbours__return_indices editednearestneighbours__return_indices: False +@ editednearestneighbours__sampling_strategy editednearestneighbours__sampling_strategy: 'auto' +@ randomforestclassifier__bootstrap randomforestclassifier__bootstrap: True +@ randomforestclassifier__class_weight randomforestclassifier__class_weight: None +@ randomforestclassifier__criterion randomforestclassifier__criterion: 'gini' +@ randomforestclassifier__max_depth randomforestclassifier__max_depth: None +@ randomforestclassifier__max_features randomforestclassifier__max_features: 'auto' +@ randomforestclassifier__max_leaf_nodes randomforestclassifier__max_leaf_nodes: None +@ randomforestclassifier__min_impurity_decrease randomforestclassifier__min_impurity_decrease: 0.0 +@ randomforestclassifier__min_impurity_split randomforestclassifier__min_impurity_split: None +@ randomforestclassifier__min_samples_leaf randomforestclassifier__min_samples_leaf: 1 +@ randomforestclassifier__min_samples_split randomforestclassifier__min_samples_split: 2 +@ randomforestclassifier__min_weight_fraction_leaf randomforestclassifier__min_weight_fraction_leaf: 0.0 +@ randomforestclassifier__n_estimators randomforestclassifier__n_estimators: 'warn' +* randomforestclassifier__n_jobs randomforestclassifier__n_jobs: 1 +@ randomforestclassifier__oob_score randomforestclassifier__oob_score: False +@ randomforestclassifier__random_state randomforestclassifier__random_state: None +* randomforestclassifier__verbose randomforestclassifier__verbose: 0 +@ randomforestclassifier__warm_start randomforestclassifier__warm_start: False + Note: @, searchable params in searchcv too. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/get_params12.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params12.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,32 @@ + Parameter Value +@ estimator "estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='reg:linear', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1)" +@ n_features_to_select n_features_to_select: None +* step step: 1 +* verbose verbose: 0 +@ estimator__base_score estimator__base_score: 0.5 +@ estimator__booster estimator__booster: 'gbtree' +@ estimator__colsample_bylevel estimator__colsample_bylevel: 1 +@ estimator__colsample_bytree estimator__colsample_bytree: 1 +@ estimator__gamma estimator__gamma: 0 +@ estimator__learning_rate estimator__learning_rate: 0.1 +@ estimator__max_delta_step estimator__max_delta_step: 0 +@ estimator__max_depth estimator__max_depth: 3 +@ estimator__min_child_weight estimator__min_child_weight: 1 +@ estimator__missing estimator__missing: nan +@ estimator__n_estimators estimator__n_estimators: 100 +* estimator__n_jobs estimator__n_jobs: 1 +* estimator__nthread estimator__nthread: None +@ estimator__objective estimator__objective: 'reg:linear' +@ estimator__random_state estimator__random_state: 0 +@ estimator__reg_alpha estimator__reg_alpha: 0 +@ estimator__reg_lambda estimator__reg_lambda: 1 +@ estimator__scale_pos_weight estimator__scale_pos_weight: 1 +@ estimator__seed estimator__seed: None +@ estimator__silent estimator__silent: True +@ estimator__subsample estimator__subsample: 1 + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model01 Binary file test-data/glm_model01 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model02 Binary file test-data/glm_model02 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model03 Binary file test-data/glm_model03 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model04 Binary file test-data/glm_model04 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model05 Binary file test-data/glm_model05 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model06 Binary file test-data/glm_model06 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model07 Binary file test-data/glm_model07 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_model08 Binary file test-data/glm_model08 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/glm_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result01 Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,5 @@ 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000000000000 -r 3b6ee54eb7e2 test-data/keras01.json --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras01.json Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,90 @@ +{ + "class_name": "Sequential", + "config": { + "name": "sequential_1", + "layers": [ + { + "class_name": "Dense", + "config": { + "name": "dense_1", + "trainable": true, + "batch_input_shape": [ + null, + 784 + ], + "dtype": "float32", + "units": 32, + "activation": "linear", + "use_bias": true, + "kernel_initializer": { + "class_name": "VarianceScaling", + "config": { + "scale": 1.0, + "mode": "fan_avg", + "distribution": "uniform", + "seed": null + } + }, + "bias_initializer": { + "class_name": "Zeros", + "config": {} + }, + "kernel_regularizer": null, + "bias_regularizer": null, + "activity_regularizer": null, + "kernel_constraint": null, + "bias_constraint": null + } + }, + { + "class_name": "Activation", + "config": { + "name": "activation_1", + "trainable": true, + "dtype": "float32", + "activation": "relu" 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"class_name": "Model", + "config": { + "name": "model_1", + "layers": [ + { + "name": "main_input", + "class_name": "InputLayer", + "config": { + "batch_input_shape": [ + null, + 100 + ], + "dtype": "int32", + "sparse": false, + "name": "main_input" + }, + "inbound_nodes": [] + }, + { + "name": "embedding_1", + "class_name": "Embedding", + "config": { + "name": "embedding_1", + "trainable": true, + "batch_input_shape": [ + null, + 100 + ], + "dtype": "float32", + "input_dim": 10000, + "output_dim": 512, + "embeddings_initializer": { + "class_name": "RandomUniform", + "config": { + "minval": -0.05, + "maxval": 0.05, + "seed": null + } + }, + "embeddings_regularizer": null, + "activity_regularizer": null, + "embeddings_constraint": null, + "mask_zero": false, + "input_length": 100 + }, + "inbound_nodes": [ + [ + [ + "main_input", + 0, + 0, + {} + ] + ] + ] + }, + { + "name": "lstm_1", + "class_name": "LSTM", + "config": { + "name": "lstm_1", + "trainable": true, + "dtype": "float32", + 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"config": { + "name": "dense_1", + "trainable": true, + "dtype": "float32", + "units": 1, + "activation": "sigmoid", + "use_bias": true, + "kernel_initializer": { + "class_name": "VarianceScaling", + "config": { + "scale": 1.0, + "mode": "fan_avg", + "distribution": "uniform", + "seed": null + } + }, + "bias_initializer": { + "class_name": "Zeros", + "config": {} + }, + "kernel_regularizer": null, + "bias_regularizer": null, + "activity_regularizer": null, + "kernel_constraint": null, + "bias_constraint": null + }, + "inbound_nodes": [ + [ + [ + "lstm_1", + 0, + 0, + {} + ] + ] + ] + }, + { + "name": "aux_input", + "class_name": "InputLayer", + "config": { + "batch_input_shape": [ + null, + 5 + ], + "dtype": "float32", + "sparse": false, + "name": "aux_input" + }, + "inbound_nodes": [] + }, + { + "name": "concatenate_1", + "class_name": "Concatenate", + "config": { + "name": "concatenate_1", + "trainable": true, + "dtype": "float32", + "axis": -1 + }, + "inbound_nodes": [ + [ + [ + 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0, + {} + ] + ] + ] + }, + { + "name": "dense_5", + "class_name": "Dense", + "config": { + "name": "dense_5", + "trainable": true, + "dtype": "float32", + "units": 1, + "activation": "sigmoid", + "use_bias": true, + "kernel_initializer": { + "class_name": "VarianceScaling", + "config": { + "scale": 1.0, + "mode": "fan_avg", + "distribution": "uniform", + "seed": null + } + }, + "bias_initializer": { + "class_name": "Zeros", + "config": {} + }, + "kernel_regularizer": null, + "bias_regularizer": null, + "activity_regularizer": null, + "kernel_constraint": null, + "bias_constraint": null + }, + "inbound_nodes": [ + [ + [ + "dense_4", + 0, + 0, + {} + ] + ] + ] + } + ], + "input_layers": [ + [ + "main_input", + 0, + 0 + ], + [ + "aux_input", + 0, + 0 + ] + ], + "output_layers": [ + [ + "dense_1", + 0, + 0 + ], + [ + "dense_5", + 0, + 0 + ] + ] + }, + "keras_version": "2.3.1", + "backend": "tensorflow" +} \ No newline at end of file diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras03.json --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras03.json Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,1 @@ +{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 17], "dtype": "float32", "units": 100, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": 0}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.1, "noise_shape": null, "seed": 0}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": 0}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.2.4", "backend": "tensorflow"} \ No newline at end of file diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras04.json --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras04.json Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,90 @@ +{ + "class_name": "Sequential", + "config": { + "name": "sequential_1", + "layers": [ + { + "class_name": "Dense", + "config": { + "name": "dense_1", + "trainable": true, + "batch_input_shape": [ + null, + 17 + ], + "dtype": "float32", + "units": 32, + "activation": "linear", + "use_bias": true, + "kernel_initializer": { + "class_name": "VarianceScaling", + "config": { + "scale": 1.0, + "mode": "fan_avg", + "distribution": "uniform", + "seed": null + } + }, + "bias_initializer": { + "class_name": "Zeros", + "config": {} + }, + "kernel_regularizer": null, + "bias_regularizer": null, + "activity_regularizer": null, + "kernel_constraint": null, + "bias_constraint": null + } + }, + { + "class_name": "Activation", + "config": { + "name": "activation_1", + "trainable": true, + "dtype": "float32", + "activation": "linear" + } + }, + { + "class_name": "Dense", + "config": { + "name": "dense_2", + "trainable": true, + "dtype": "float32", + "units": 1, + "activation": "linear", + "use_bias": true, + "kernel_initializer": { + "class_name": "VarianceScaling", + "config": { + "scale": 1.0, + "mode": "fan_avg", + "distribution": "uniform", + "seed": null + } + }, + "bias_initializer": { + "class_name": "Zeros", + "config": {} + }, + "kernel_regularizer": null, + "bias_regularizer": null, + "activity_regularizer": null, + "kernel_constraint": null, + "bias_constraint": null + } + }, + { + "class_name": "Activation", + "config": { + "name": "activation_2", + "trainable": true, + "dtype": "float32", + "activation": "linear" + } + } + ] + }, + "keras_version": "2.3.1", + "backend": "tensorflow" +} \ No newline at end of file diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_batch_model01 Binary file test-data/keras_batch_model01 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_batch_model02 Binary file test-data/keras_batch_model02 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_batch_model03 Binary file test-data/keras_batch_model03 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_batch_model04 Binary file test-data/keras_batch_model04 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_batch_params01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras_batch_params01.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,96 @@ + Parameter Value +@ amsgrad amsgrad: None +@ batch_size batch_size: 32 +@ beta_1 beta_1: None +@ beta_2 beta_2: None +@ callbacks callbacks: [{'callback_selection': {'callback_type': 'None'}}] +@ class_positive_factor class_positive_factor: 1.0 +@ config config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable +@ data_batch_generator "data_batch_generator: FastaDNABatchGenerator(fasta_path='to_be_determined', seed=999, seq_length=1000, + shuffle=True)" +@ decay decay: 0.0 +@ epochs epochs: 100 +@ layers_0_Dense layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, +@ layers_1_Activation layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32 +@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units' +@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32 +@ loss loss: 'binary_crossentropy' +@ lr lr: 0.01 +@ metrics metrics: ['acc'] +@ model_type model_type: 'sequential' +@ momentum momentum: 0.0 +* n_jobs n_jobs: 1 +@ nesterov nesterov: False +@ optimizer optimizer: 'sgd' +@ prediction_steps prediction_steps: None +@ rho rho: None +@ schedule_decay schedule_decay: None +@ seed seed: None +@ steps_per_epoch steps_per_epoch: None +@ validation_fraction validation_fraction: 0.1 +@ validation_steps validation_steps: None +@ verbose verbose: 0 +* data_batch_generator__fasta_path data_batch_generator__fasta_path: 'to_be_determined' +@ data_batch_generator__seed data_batch_generator__seed: 999 +@ data_batch_generator__seq_length data_batch_generator__seq_length: 1000 +@ data_batch_generator__shuffle data_batch_generator__shuffle: True +* layers_0_Dense__class_name layers_0_Dense__class_name: 'Dense' +@ layers_0_Dense__config layers_0_Dense__config: {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, 784], 'dtype': 'float32', 'units' +@ layers_0_Dense__config__activation layers_0_Dense__config__activation: 'linear' +@ layers_0_Dense__config__activity_regularizer layers_0_Dense__config__activity_regularizer: None +@ layers_0_Dense__config__batch_input_shape layers_0_Dense__config__batch_input_shape: [None, 784] +@ layers_0_Dense__config__bias_constraint layers_0_Dense__config__bias_constraint: None +@ layers_0_Dense__config__bias_initializer layers_0_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}} +* layers_0_Dense__config__bias_initializer__class_name layers_0_Dense__config__bias_initializer__class_name: 'Zeros' +@ layers_0_Dense__config__bias_initializer__config layers_0_Dense__config__bias_initializer__config: {} +@ layers_0_Dense__config__bias_regularizer layers_0_Dense__config__bias_regularizer: None +@ layers_0_Dense__config__dtype layers_0_Dense__config__dtype: 'float32' +@ layers_0_Dense__config__kernel_constraint layers_0_Dense__config__kernel_constraint: None +@ layers_0_Dense__config__kernel_initializer layers_0_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo +* layers_0_Dense__config__kernel_initializer__class_name layers_0_Dense__config__kernel_initializer__class_name: 'VarianceScaling' +@ layers_0_Dense__config__kernel_initializer__config layers_0_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None} +@ layers_0_Dense__config__kernel_initializer__config__distribution layers_0_Dense__config__kernel_initializer__config__distribution: 'uniform' +@ layers_0_Dense__config__kernel_initializer__config__mode layers_0_Dense__config__kernel_initializer__config__mode: 'fan_avg' +@ layers_0_Dense__config__kernel_initializer__config__scale layers_0_Dense__config__kernel_initializer__config__scale: 1.0 +@ layers_0_Dense__config__kernel_initializer__config__seed layers_0_Dense__config__kernel_initializer__config__seed: None +@ layers_0_Dense__config__kernel_regularizer layers_0_Dense__config__kernel_regularizer: None +* layers_0_Dense__config__name layers_0_Dense__config__name: 'dense_1' +@ layers_0_Dense__config__trainable layers_0_Dense__config__trainable: True +@ layers_0_Dense__config__units layers_0_Dense__config__units: 32 +@ layers_0_Dense__config__use_bias layers_0_Dense__config__use_bias: True +* layers_1_Activation__class_name layers_1_Activation__class_name: 'Activation' +@ layers_1_Activation__config layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'} +@ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'relu' +@ layers_1_Activation__config__dtype layers_1_Activation__config__dtype: 'float32' +* layers_1_Activation__config__name layers_1_Activation__config__name: 'activation_1' +@ layers_1_Activation__config__trainable layers_1_Activation__config__trainable: True +* layers_2_Dense__class_name layers_2_Dense__class_name: 'Dense' +@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'linear', 'use +@ layers_2_Dense__config__activation layers_2_Dense__config__activation: 'linear' +@ layers_2_Dense__config__activity_regularizer layers_2_Dense__config__activity_regularizer: None +@ layers_2_Dense__config__bias_constraint layers_2_Dense__config__bias_constraint: None +@ layers_2_Dense__config__bias_initializer layers_2_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}} +* layers_2_Dense__config__bias_initializer__class_name layers_2_Dense__config__bias_initializer__class_name: 'Zeros' +@ layers_2_Dense__config__bias_initializer__config layers_2_Dense__config__bias_initializer__config: {} +@ layers_2_Dense__config__bias_regularizer layers_2_Dense__config__bias_regularizer: None +@ layers_2_Dense__config__dtype layers_2_Dense__config__dtype: 'float32' +@ layers_2_Dense__config__kernel_constraint layers_2_Dense__config__kernel_constraint: None +@ layers_2_Dense__config__kernel_initializer layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo +* layers_2_Dense__config__kernel_initializer__class_name layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling' +@ layers_2_Dense__config__kernel_initializer__config layers_2_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None} +@ layers_2_Dense__config__kernel_initializer__config__distribution layers_2_Dense__config__kernel_initializer__config__distribution: 'uniform' +@ layers_2_Dense__config__kernel_initializer__config__mode layers_2_Dense__config__kernel_initializer__config__mode: 'fan_avg' +@ layers_2_Dense__config__kernel_initializer__config__scale layers_2_Dense__config__kernel_initializer__config__scale: 1.0 +@ layers_2_Dense__config__kernel_initializer__config__seed layers_2_Dense__config__kernel_initializer__config__seed: None +@ layers_2_Dense__config__kernel_regularizer layers_2_Dense__config__kernel_regularizer: None +* layers_2_Dense__config__name layers_2_Dense__config__name: 'dense_2' +@ layers_2_Dense__config__trainable layers_2_Dense__config__trainable: True +@ layers_2_Dense__config__units layers_2_Dense__config__units: 10 +@ layers_2_Dense__config__use_bias layers_2_Dense__config__use_bias: True +* layers_3_Activation__class_name layers_3_Activation__class_name: 'Activation' +@ layers_3_Activation__config layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'softmax'} +@ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'softmax' +@ layers_3_Activation__config__dtype layers_3_Activation__config__dtype: 'float32' +* layers_3_Activation__config__name layers_3_Activation__config__name: 'activation_2' +@ layers_3_Activation__config__trainable layers_3_Activation__config__trainable: True + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_batch_params04.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras_batch_params04.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,91 @@ + Parameter Value +@ amsgrad amsgrad: None +@ batch_size batch_size: 32 +@ beta_1 beta_1: None +@ beta_2 beta_2: None +@ callbacks callbacks: [{'callback_selection': {'callback_type': 'None'}}] +@ class_positive_factor class_positive_factor: 1.0 +@ config config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable +@ data_batch_generator data_batch_generator: None +@ decay decay: 0.0 +@ epochs epochs: 100 +@ layers_0_Dense layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, +@ layers_1_Activation layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32 +@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units' +@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32 +@ loss loss: 'binary_crossentropy' +@ lr lr: 0.01 +@ metrics metrics: ['acc'] +@ model_type model_type: 'sequential' +@ momentum momentum: 0.0 +* n_jobs n_jobs: 1 +@ nesterov nesterov: False +@ optimizer optimizer: 'sgd' +@ prediction_steps prediction_steps: None +@ rho rho: None +@ schedule_decay schedule_decay: None +@ seed seed: None +@ steps_per_epoch steps_per_epoch: None +@ validation_fraction validation_fraction: 0.1 +@ validation_steps validation_steps: None +@ verbose verbose: 0 +* layers_0_Dense__class_name layers_0_Dense__class_name: 'Dense' +@ layers_0_Dense__config layers_0_Dense__config: {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, 784], 'dtype': 'float32', 'units' +@ layers_0_Dense__config__activation layers_0_Dense__config__activation: 'linear' +@ layers_0_Dense__config__activity_regularizer layers_0_Dense__config__activity_regularizer: None +@ layers_0_Dense__config__batch_input_shape layers_0_Dense__config__batch_input_shape: [None, 784] +@ layers_0_Dense__config__bias_constraint layers_0_Dense__config__bias_constraint: None +@ layers_0_Dense__config__bias_initializer layers_0_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}} +* layers_0_Dense__config__bias_initializer__class_name layers_0_Dense__config__bias_initializer__class_name: 'Zeros' +@ layers_0_Dense__config__bias_initializer__config layers_0_Dense__config__bias_initializer__config: {} +@ layers_0_Dense__config__bias_regularizer layers_0_Dense__config__bias_regularizer: None +@ layers_0_Dense__config__dtype layers_0_Dense__config__dtype: 'float32' +@ layers_0_Dense__config__kernel_constraint layers_0_Dense__config__kernel_constraint: None +@ layers_0_Dense__config__kernel_initializer layers_0_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo +* layers_0_Dense__config__kernel_initializer__class_name layers_0_Dense__config__kernel_initializer__class_name: 'VarianceScaling' +@ layers_0_Dense__config__kernel_initializer__config layers_0_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None} +@ layers_0_Dense__config__kernel_initializer__config__distribution layers_0_Dense__config__kernel_initializer__config__distribution: 'uniform' +@ layers_0_Dense__config__kernel_initializer__config__mode layers_0_Dense__config__kernel_initializer__config__mode: 'fan_avg' +@ layers_0_Dense__config__kernel_initializer__config__scale layers_0_Dense__config__kernel_initializer__config__scale: 1.0 +@ layers_0_Dense__config__kernel_initializer__config__seed layers_0_Dense__config__kernel_initializer__config__seed: None +@ layers_0_Dense__config__kernel_regularizer layers_0_Dense__config__kernel_regularizer: None +* layers_0_Dense__config__name layers_0_Dense__config__name: 'dense_1' +@ layers_0_Dense__config__trainable layers_0_Dense__config__trainable: True +@ layers_0_Dense__config__units layers_0_Dense__config__units: 32 +@ layers_0_Dense__config__use_bias layers_0_Dense__config__use_bias: True +* layers_1_Activation__class_name layers_1_Activation__class_name: 'Activation' +@ layers_1_Activation__config layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'} +@ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'relu' +@ layers_1_Activation__config__dtype layers_1_Activation__config__dtype: 'float32' +* layers_1_Activation__config__name layers_1_Activation__config__name: 'activation_1' +@ layers_1_Activation__config__trainable layers_1_Activation__config__trainable: True +* layers_2_Dense__class_name layers_2_Dense__class_name: 'Dense' +@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'linear', 'use +@ layers_2_Dense__config__activation layers_2_Dense__config__activation: 'linear' +@ layers_2_Dense__config__activity_regularizer layers_2_Dense__config__activity_regularizer: None +@ layers_2_Dense__config__bias_constraint layers_2_Dense__config__bias_constraint: None +@ layers_2_Dense__config__bias_initializer layers_2_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}} +* layers_2_Dense__config__bias_initializer__class_name layers_2_Dense__config__bias_initializer__class_name: 'Zeros' +@ layers_2_Dense__config__bias_initializer__config layers_2_Dense__config__bias_initializer__config: {} +@ layers_2_Dense__config__bias_regularizer layers_2_Dense__config__bias_regularizer: None +@ layers_2_Dense__config__dtype layers_2_Dense__config__dtype: 'float32' +@ layers_2_Dense__config__kernel_constraint layers_2_Dense__config__kernel_constraint: None +@ layers_2_Dense__config__kernel_initializer layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo +* layers_2_Dense__config__kernel_initializer__class_name layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling' +@ layers_2_Dense__config__kernel_initializer__config layers_2_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None} +@ layers_2_Dense__config__kernel_initializer__config__distribution layers_2_Dense__config__kernel_initializer__config__distribution: 'uniform' +@ layers_2_Dense__config__kernel_initializer__config__mode layers_2_Dense__config__kernel_initializer__config__mode: 'fan_avg' +@ layers_2_Dense__config__kernel_initializer__config__scale layers_2_Dense__config__kernel_initializer__config__scale: 1.0 +@ layers_2_Dense__config__kernel_initializer__config__seed layers_2_Dense__config__kernel_initializer__config__seed: None +@ layers_2_Dense__config__kernel_regularizer layers_2_Dense__config__kernel_regularizer: None +* layers_2_Dense__config__name layers_2_Dense__config__name: 'dense_2' +@ layers_2_Dense__config__trainable layers_2_Dense__config__trainable: True +@ layers_2_Dense__config__units layers_2_Dense__config__units: 10 +@ layers_2_Dense__config__use_bias layers_2_Dense__config__use_bias: True +* layers_3_Activation__class_name layers_3_Activation__class_name: 'Activation' +@ layers_3_Activation__config layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'softmax'} +@ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'softmax' +@ layers_3_Activation__config__dtype layers_3_Activation__config__dtype: 'float32' +* layers_3_Activation__config__name layers_3_Activation__config__name: 'activation_2' +@ layers_3_Activation__config__trainable layers_3_Activation__config__trainable: True + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_model01 Binary file test-data/keras_model01 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_model02 Binary file test-data/keras_model02 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_model04 Binary file test-data/keras_model04 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_params04.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras_params04.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,87 @@ + Parameter Value +@ amsgrad amsgrad: False +@ batch_size batch_size: 32 +@ beta_1 beta_1: 0.9 +@ beta_2 beta_2: 0.999 +@ callbacks callbacks: [{'callback_selection': {'callback_type': 'None'}}] +@ config config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable +@ decay decay: 0.0 +@ epochs epochs: 100 +@ layers_0_Dense layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, +@ layers_1_Activation layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32 +@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units' +@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32 +@ loss loss: 'mean_squared_error' +@ lr lr: 0.001 +@ metrics metrics: ['mse'] +@ model_type model_type: 'sequential' +@ momentum momentum: None +@ nesterov nesterov: None +@ optimizer optimizer: 'adam' +@ rho rho: None +@ schedule_decay schedule_decay: None +@ seed seed: 42 +@ steps_per_epoch steps_per_epoch: None +@ validation_fraction validation_fraction: 0.1 +@ validation_steps validation_steps: None +@ verbose verbose: 0 +* layers_0_Dense__class_name layers_0_Dense__class_name: 'Dense' +@ layers_0_Dense__config layers_0_Dense__config: {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None, 17], 'dtype': 'float32', 'units': +@ layers_0_Dense__config__activation layers_0_Dense__config__activation: 'linear' +@ layers_0_Dense__config__activity_regularizer layers_0_Dense__config__activity_regularizer: None +@ layers_0_Dense__config__batch_input_shape layers_0_Dense__config__batch_input_shape: [None, 17] +@ layers_0_Dense__config__bias_constraint layers_0_Dense__config__bias_constraint: None +@ layers_0_Dense__config__bias_initializer layers_0_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}} +* layers_0_Dense__config__bias_initializer__class_name layers_0_Dense__config__bias_initializer__class_name: 'Zeros' +@ layers_0_Dense__config__bias_initializer__config layers_0_Dense__config__bias_initializer__config: {} +@ layers_0_Dense__config__bias_regularizer layers_0_Dense__config__bias_regularizer: None +@ layers_0_Dense__config__dtype layers_0_Dense__config__dtype: 'float32' +@ layers_0_Dense__config__kernel_constraint layers_0_Dense__config__kernel_constraint: None +@ layers_0_Dense__config__kernel_initializer layers_0_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo +* layers_0_Dense__config__kernel_initializer__class_name layers_0_Dense__config__kernel_initializer__class_name: 'VarianceScaling' +@ layers_0_Dense__config__kernel_initializer__config layers_0_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None} +@ layers_0_Dense__config__kernel_initializer__config__distribution layers_0_Dense__config__kernel_initializer__config__distribution: 'uniform' +@ layers_0_Dense__config__kernel_initializer__config__mode layers_0_Dense__config__kernel_initializer__config__mode: 'fan_avg' +@ layers_0_Dense__config__kernel_initializer__config__scale layers_0_Dense__config__kernel_initializer__config__scale: 1.0 +@ layers_0_Dense__config__kernel_initializer__config__seed layers_0_Dense__config__kernel_initializer__config__seed: None +@ layers_0_Dense__config__kernel_regularizer layers_0_Dense__config__kernel_regularizer: None +* layers_0_Dense__config__name layers_0_Dense__config__name: 'dense_1' +@ layers_0_Dense__config__trainable layers_0_Dense__config__trainable: True +@ layers_0_Dense__config__units layers_0_Dense__config__units: 32 +@ layers_0_Dense__config__use_bias layers_0_Dense__config__use_bias: True +* layers_1_Activation__class_name layers_1_Activation__class_name: 'Activation' +@ layers_1_Activation__config layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'linear'} +@ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'linear' +@ layers_1_Activation__config__dtype layers_1_Activation__config__dtype: 'float32' +* layers_1_Activation__config__name layers_1_Activation__config__name: 'activation_1' +@ layers_1_Activation__config__trainable layers_1_Activation__config__trainable: True +* layers_2_Dense__class_name layers_2_Dense__class_name: 'Dense' +@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_ +@ layers_2_Dense__config__activation layers_2_Dense__config__activation: 'linear' +@ layers_2_Dense__config__activity_regularizer layers_2_Dense__config__activity_regularizer: None +@ layers_2_Dense__config__bias_constraint layers_2_Dense__config__bias_constraint: None +@ layers_2_Dense__config__bias_initializer layers_2_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}} +* layers_2_Dense__config__bias_initializer__class_name layers_2_Dense__config__bias_initializer__class_name: 'Zeros' +@ layers_2_Dense__config__bias_initializer__config layers_2_Dense__config__bias_initializer__config: {} +@ layers_2_Dense__config__bias_regularizer layers_2_Dense__config__bias_regularizer: None +@ layers_2_Dense__config__dtype layers_2_Dense__config__dtype: 'float32' +@ layers_2_Dense__config__kernel_constraint layers_2_Dense__config__kernel_constraint: None +@ layers_2_Dense__config__kernel_initializer layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo +* layers_2_Dense__config__kernel_initializer__class_name layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling' +@ layers_2_Dense__config__kernel_initializer__config layers_2_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None} +@ layers_2_Dense__config__kernel_initializer__config__distribution layers_2_Dense__config__kernel_initializer__config__distribution: 'uniform' +@ layers_2_Dense__config__kernel_initializer__config__mode layers_2_Dense__config__kernel_initializer__config__mode: 'fan_avg' +@ layers_2_Dense__config__kernel_initializer__config__scale layers_2_Dense__config__kernel_initializer__config__scale: 1.0 +@ layers_2_Dense__config__kernel_initializer__config__seed layers_2_Dense__config__kernel_initializer__config__seed: None +@ layers_2_Dense__config__kernel_regularizer layers_2_Dense__config__kernel_regularizer: None +* layers_2_Dense__config__name layers_2_Dense__config__name: 'dense_2' +@ layers_2_Dense__config__trainable layers_2_Dense__config__trainable: True +@ layers_2_Dense__config__units layers_2_Dense__config__units: 1 +@ layers_2_Dense__config__use_bias layers_2_Dense__config__use_bias: True +* layers_3_Activation__class_name layers_3_Activation__class_name: 'Activation' +@ layers_3_Activation__config layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'linear'} +@ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'linear' +@ layers_3_Activation__config__dtype layers_3_Activation__config__dtype: 'float32' +* layers_3_Activation__config__name layers_3_Activation__config__name: 'activation_2' +@ layers_3_Activation__config__trainable layers_3_Activation__config__trainable: True + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_prefitted01.zip Binary file test-data/keras_prefitted01.zip has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_save_weights01.h5 Binary file test-data/keras_save_weights01.h5 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/keras_train_eval_y_true02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras_train_eval_y_true02.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,54 @@ +0 +54 +54 +41 +48 +46 +74 +57 +52 +54 +54 +45 +57 +54 +51 +68 +71 +68 +68 +40 +46 +79 +46 +49 +55 +68 +76 +85 +42 +79 +77 +80 +64 +59 +48 +67 +50 +77 +88 +76 +75 +66 +61 +89 +49 +59 +71 +60 +55 +77 +75 +54 +75 +60 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/lda_model01 Binary file test-data/lda_model01 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/lda_model02 Binary file test-data/lda_model02 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/lda_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lda_prediction_result01.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/lda_prediction_result02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lda_prediction_result02.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/le_input_w_header.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/le_input_w_header.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,5 @@ +Class +Liverpool +Real Madrid +Bayern Munich +A.C. Milan diff -r 000000000000 -r 3b6ee54eb7e2 test-data/le_input_wo_header.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/le_input_wo_header.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,4 @@ +Liverpool +Real Madrid +Bayern Munich +A.C. Milan diff -r 000000000000 -r 3b6ee54eb7e2 test-data/le_output.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/le_output.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,4 @@ +2 +3 +1 +0 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/lgb_class_model.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lgb_class_model.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,151 @@ +tree +version=v3 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=3 +objective=binary sigmoid:1 +feature_names=Column_0 Column_1 Column_2 Column_3 +feature_infos=none none none none +tree_sizes=228 + +Tree=0 +num_leaves=1 +num_cat=0 +split_feature= +split_gain= +threshold= +decision_type= +left_child= +right_child= +leaf_value=-0.40546510810816427 +leaf_weight= +leaf_count= +internal_value= +internal_weight= +internal_count= +shrinkage=1 + + +end of trees + +feature importances: + +parameters: +[boosting: gbdt] +[objective: binary] +[metric: binary_logloss] +[tree_learner: serial] +[device_type: cpu] +[data: ] +[valid: ] +[num_iterations: 100] +[learning_rate: 0.02] +[num_leaves: 32] +[num_threads: 0] +[max_depth: 8] +[min_data_in_leaf: 20] +[min_sum_hessian_in_leaf: 39] +[bagging_fraction: 0.9] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 0] +[bagging_seed: 18467] +[feature_fraction: 0.9] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 26500] +[early_stopping_round: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0.04] +[lambda_l2: 0.07] +[min_gain_to_split: 0.02] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 6334] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[feature_contri: ] +[forcedsplits_filename: ] +[forcedbins_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[verbosity: -1] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[histogram_pool_size: -1] +[data_random_seed: 41] +[output_model: LightGBM_model.txt] +[snapshot_freq: -1] +[input_model: ] +[output_result: LightGBM_predict_result.txt] +[initscore_filename: ] +[valid_data_initscores: ] +[pre_partition: 0] +[enable_bundle: 1] +[max_conflict_rate: 0] +[is_enable_sparse: 1] +[sparse_threshold: 0.8] +[use_missing: 1] +[zero_as_missing: 0] +[two_round: 0] +[save_binary: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[predict_raw_score: 0] +[predict_leaf_index: 0] +[predict_contrib: 0] +[num_iteration_predict: -1] +[pred_early_stop: 0] +[pred_early_stop_freq: 10] +[pred_early_stop_margin: 10] +[convert_model_language: ] +[convert_model: gbdt_prediction.cpp] +[num_class: 1] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[max_position: 20] +[lambdamart_norm: 1] +[label_gain: ] +[metric_freq: 1] +[is_provide_training_metric: 0] +[eval_at: ] +[multi_error_top_k: 1] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] + +end of parameters + +pandas_categorical:null diff -r 000000000000 -r 3b6ee54eb7e2 test-data/lgb_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lgb_prediction_result01.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,262 @@ +year month day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend week_Fri week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed predicted +2016 9 19 68 69 69.7 65 74 71 88 0 1 0 0 0 0 0 71.89319490976423 +2016 4 14 60 59 58.1 57 63 58 66 0 0 0 0 1 0 0 59.01499037390416 +2016 7 30 85 88 77.3 75 79 77 70 0 0 1 0 0 0 0 75.7624470867011 +2016 5 15 82 65 64.7 63 69 64 58 0 0 0 1 0 0 0 57.569131115445174 +2016 1 18 54 50 47.5 44 48 49 58 0 1 0 0 0 0 0 53.09785655110459 +2016 1 25 48 51 48.2 45 51 49 63 0 1 0 0 0 0 0 53.51723077599964 +2016 11 25 49 52 48.6 45 52 47 41 1 0 0 0 0 0 0 51.95292617354113 +2016 7 20 73 78 76.7 75 78 77 66 0 0 0 0 0 0 1 80.03391243189029 +2016 12 17 39 35 45.2 43 47 46 38 0 0 1 0 0 0 0 38.021020662843554 +2016 12 8 42 40 46.1 45 51 47 36 0 0 0 0 1 0 0 43.871817980640564 +2016 12 28 42 47 45.3 41 49 44 58 0 0 0 0 0 0 1 45.76312225952035 +2016 7 17 76 72 76.3 76 78 77 88 0 0 0 1 0 0 0 78.44319295537714 +2016 7 7 69 76 74.4 73 77 74 72 0 0 0 0 1 0 0 70.5335293567219 +2016 12 15 40 39 45.3 45 49 47 46 0 0 0 0 1 0 0 39.057420195088596 +2016 6 27 71 78 72.2 70 74 72 84 0 1 0 0 0 0 0 82.20245159198711 +2016 5 31 64 71 67.3 63 72 68 85 0 0 0 0 0 1 0 78.30191181424183 +2016 1 20 54 48 47.7 44 52 49 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+threshold=55.500000000000007 41.500000000000007 61.500000000000007 19.500000000000004 +decision_type=2 2 2 2 +left_child=1 -1 -2 -4 +right_child=2 -3 3 -5 +leaf_value=-0.00068129068586066284 -0.0016364292283236189 0.0015344814289685333 -0.00084929386379414688 0.0018615465409957174 +leaf_weight=43 46 52 73 47 +leaf_count=43 46 52 73 47 +internal_value=0 0.0261687 -0.0150013 0.0103128 +internal_weight=0 95 166 120 +internal_count=261 95 166 120 +shrinkage=0.02 + + +Tree=9993 +num_leaves=6 +num_cat=0 +split_feature=9 2 5 4 3 +split_gain=0.083992 0.358298 0.507625 0.369569 0.259852 +threshold=42.500000000000007 12.500000000000002 53.95000000000001 67.500000000000014 66.500000000000014 +decision_type=2 2 2 2 2 +left_child=-1 3 -3 -2 -4 +right_child=1 2 4 -5 -6 +leaf_value=0.00082939195546691854 0.0022114901908763889 -0.0027786929063854117 0.0012537082030488183 -0.00051513148030428977 -0.0010046257933522501 +leaf_weight=49 42 40 39 41 50 +leaf_count=49 42 40 39 41 50 +internal_value=0 -0.00961487 -0.0436435 0.0427838 -0.000291475 +internal_weight=0 212 129 83 89 +internal_count=261 212 129 83 89 +shrinkage=0.02 + + +Tree=9994 +num_leaves=5 +num_cat=0 +split_feature=9 1 9 6 +split_gain=0.082182 0.231743 0.318784 0.114402 +threshold=67.500000000000014 9.5000000000000018 56.500000000000007 67.500000000000014 +decision_type=2 2 2 2 +left_child=1 2 -1 -2 +right_child=3 -3 -4 -5 +leaf_value=-0.00010134627320867472 0.00015728727400315828 -0.00070247958287017345 0.0021242061802477655 -0.001413439896737723 +leaf_weight=62 46 65 48 40 +leaf_count=62 46 65 48 40 +internal_value=0 0.0138544 0.0431635 -0.0282272 +internal_weight=0 175 110 86 +internal_count=261 175 110 86 +shrinkage=0.02 + + +Tree=9995 +num_leaves=5 +num_cat=0 +split_feature=3 1 4 9 +split_gain=0.0820278 0.500473 0.991657 0.429361 +threshold=52.500000000000007 6.5000000000000009 69.500000000000014 66.500000000000014 +decision_type=2 2 2 2 +left_child=-1 3 -3 -2 +right_child=1 2 -4 -5 +leaf_value=0.00075566499665265241 -0.0023154336288241958 0.0027189359208700547 -0.0011991007566317525 0.0004167319863218464 +leaf_weight=56 61 53 52 39 +leaf_count=56 61 53 52 39 +internal_value=0 -0.0103538 0.0385727 -0.0621174 +internal_weight=0 205 105 100 +internal_count=261 205 105 100 +shrinkage=0.02 + + +Tree=9996 +num_leaves=5 +num_cat=0 +split_feature=1 2 4 2 +split_gain=0.0891822 0.619853 1.25777 0.484518 +threshold=8.5000000000000018 17.500000000000004 69.500000000000014 17.500000000000004 +decision_type=2 2 2 2 +left_child=1 2 -1 -2 +right_child=3 -3 -4 -5 +leaf_value=0.0032861471461533291 -0.0018154409283054544 -0.0011745428277196838 -0.0013307264559687503 0.001118773592702658 +leaf_weight=57 54 68 41 41 +leaf_count=57 54 68 41 41 +internal_value=0 0.0154562 0.0673435 -0.0270395 +internal_weight=0 166 98 95 +internal_count=261 166 98 95 +shrinkage=0.02 + + +Tree=9997 +num_leaves=5 +num_cat=0 +split_feature=1 2 4 2 +split_gain=0.084772 0.594484 1.20735 0.46461 +threshold=8.5000000000000018 17.500000000000004 69.500000000000014 17.500000000000004 +decision_type=2 2 2 2 +left_child=1 2 -1 -2 +right_child=3 -3 -4 -5 +leaf_value=0.0032205048776328803 -0.0017791790800006856 -0.0011510760031626303 -0.0013041575694565997 0.0010964362368637087 +leaf_weight=57 54 68 41 41 +leaf_count=57 54 68 41 41 +internal_value=0 0.0151378 0.0659898 -0.0264908 +internal_weight=0 166 98 95 +internal_count=261 166 98 95 +shrinkage=0.02 + + +Tree=9998 +num_leaves=5 +num_cat=0 +split_feature=6 9 2 1 +split_gain=0.0814517 0.423897 0.49947 0.560638 +threshold=69.500000000000014 71.500000000000014 13.500000000000002 4.5000000000000009 +decision_type=2 2 2 2 +left_child=1 2 -1 -4 +right_child=-2 -3 3 -5 +leaf_value=0.0015287226576175053 0.00082953833355114027 -0.0021162448255171214 0.0011383155735161057 -0.0019402952165041626 +leaf_weight=73 48 39 41 60 +leaf_count=73 48 39 41 60 +internal_value=0 -0.00938209 0.0120356 -0.0341362 +internal_weight=0 213 174 101 +internal_count=261 213 174 101 +shrinkage=0.02 + + +Tree=9999 +num_leaves=5 +num_cat=0 +split_feature=5 5 4 9 +split_gain=0.0787592 0.317423 0.245512 0.291236 +threshold=70.000000000000014 64.200000000000003 60.500000000000007 45.500000000000007 +decision_type=2 2 2 2 +left_child=1 2 3 -1 +right_child=-2 -3 -4 -5 +leaf_value=0.00095524065220772586 0.00069620116702481252 -0.001859639079375311 0.0015142907661079197 -0.0011629911884827296 +leaf_weight=46 62 40 44 69 +leaf_count=46 62 40 44 69 +internal_value=0 -0.0108758 0.00956023 -0.015434 +internal_weight=0 199 159 115 +internal_count=261 199 159 115 +shrinkage=0.02 + + +end of trees + +feature importances: +Column_9=8322 +Column_2=6585 +Column_5=5272 +Column_4=4915 +Column_6=4114 +Column_3=3831 +Column_1=3507 +Column_7=2717 +Column_8=2340 + +parameters: +[boosting: gbdt] +[objective: regression] +[metric: ] +[tree_learner: serial] +[device_type: cpu] +[data: ] +[valid: ] +[num_iterations: 100] +[learning_rate: 0.02] +[num_leaves: 32] +[num_threads: 0] +[max_depth: 8] +[min_data_in_leaf: 20] +[min_sum_hessian_in_leaf: 39] +[bagging_fraction: 0.9] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 0] +[bagging_seed: 18467] +[feature_fraction: 0.9] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 26500] +[early_stopping_round: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0.04] +[lambda_l2: 0.07] +[min_gain_to_split: 0.02] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 6334] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[feature_contri: ] +[forcedsplits_filename: ] +[forcedbins_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[verbosity: -1] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[histogram_pool_size: -1] +[data_random_seed: 41] +[output_model: LightGBM_model.txt] +[snapshot_freq: -1] +[input_model: ] +[output_result: LightGBM_predict_result.txt] +[initscore_filename: ] +[valid_data_initscores: ] +[pre_partition: 0] +[enable_bundle: 1] +[max_conflict_rate: 0] +[is_enable_sparse: 1] +[sparse_threshold: 0.8] +[use_missing: 1] +[zero_as_missing: 0] +[two_round: 0] +[save_binary: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[predict_raw_score: 0] +[predict_leaf_index: 0] +[predict_contrib: 0] +[num_iteration_predict: -1] +[pred_early_stop: 0] +[pred_early_stop_freq: 10] +[pred_early_stop_margin: 10] +[convert_model_language: ] +[convert_model: gbdt_prediction.cpp] +[num_class: 1] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[max_position: 20] +[lambdamart_norm: 1] +[label_gain: ] +[metric_freq: 1] +[is_provide_training_metric: 0] +[eval_at: ] +[multi_error_top_k: 1] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] + +end of parameters + +pandas_categorical:null diff -r 000000000000 -r 3b6ee54eb7e2 test-data/log_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/log_loss.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2 @@ +log_loss : +3.7248735402728403 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/matthews_corrcoef.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/matthews_corrcoef.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2 @@ +matthews_corrcoef : +1.0 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/mba_input_int_w.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mba_input_int_w.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,6 @@ +Transactions +10 11 12 13 14 15 +16 11 12 13 14 15 +10 17 13 14 +10 18 19 13 15 +19 11 11 13 20 14 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/mba_input_int_wo.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mba_input_int_wo.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,5 @@ +10 11 12 13 14 15 +16 11 12 13 14 15 +10 17 13 14 +10 18 19 13 15 +19 11 11 13 20 14 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/mba_input_str_w.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mba_input_str_w.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,6 @@ +Transactions +Milk Onion Nutmeg Kidney Beans Eggs Yogurt +Dill Onion Nutmeg Kidney Beans Eggs Yogurt +Milk Apple Kidney Beans Eggs +Milk Unicorn Corn Kidney Beans Yogurt +Corn Onion Onion Kidney Beans Ice cream Eggs diff -r 000000000000 -r 3b6ee54eb7e2 test-data/mba_input_str_wo.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mba_input_str_wo.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,5 @@ +Milk Onion Nutmeg Kidney Beans Eggs Yogurt +Dill Onion Nutmeg Kidney Beans Eggs Yogurt +Milk Apple Kidney Beans Eggs +Milk Unicorn Corn Kidney Beans Yogurt +Corn Onion Onion Kidney Beans Ice cream Eggs diff -r 000000000000 -r 3b6ee54eb7e2 test-data/mba_out_str.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mba_out_str.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,7 @@ +antecedents consequents antecedent support consequent support support confidence lift leverage conviction +['Eggs'] ['Kidney Beans', 'Onion'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['Eggs'] ['Onion'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['Eggs', 'Kidney Beans'] ['Onion'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['Kidney Beans', 'Onion'] ['Eggs'] 0.6 0.8 0.6 1.0 1.25 0.12 inf +['Onion'] ['Eggs'] 0.6 0.8 0.6 1.0 1.25 0.12 inf +['Onion'] ['Eggs', 'Kidney Beans'] 0.6 0.8 0.6 1.0 1.25 0.12 inf diff -r 000000000000 -r 3b6ee54eb7e2 test-data/mba_output_int.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mba_output_int.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,7 @@ +antecedents consequents antecedent support consequent support support confidence lift leverage conviction +['11'] ['13', '14'] 0.6 0.8 0.6 1.0 1.25 0.12 inf +['11'] ['14'] 0.6 0.8 0.6 1.0 1.25 0.12 inf +['11', '13'] ['14'] 0.6 0.8 0.6 1.0 1.25 0.12 inf +['13', '14'] ['11'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['14'] ['11'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['14'] ['11', '13'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/mba_output_str.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mba_output_str.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,7 @@ +antecedents consequents antecedent support consequent support support confidence lift leverage conviction +['Eggs'] ['Kidney Beans', 'Onion'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['Eggs'] ['Onion'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['Eggs', 'Kidney Beans'] ['Onion'] 0.8 0.6 0.6 0.7499999999999999 1.2499999999999998 0.12 1.5999999999999994 +['Kidney Beans', 'Onion'] ['Eggs'] 0.6 0.8 0.6 1.0 1.25 0.12 inf +['Onion'] ['Eggs'] 0.6 0.8 0.6 1.0 1.25 0.12 inf +['Onion'] ['Eggs', 'Kidney Beans'] 0.6 0.8 0.6 1.0 1.25 0.12 inf diff -r 000000000000 -r 3b6ee54eb7e2 test-data/ml_confusion_predicted.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ml_confusion_predicted.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,34 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 diff -r 000000000000 -r 3b6ee54eb7e2 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3.656764736071405064e-02 1.954721520949571190e+00 2.734474567634836983e-01 8.281588142887611470e-01 4.484584054747960957e-01 7.652386767723299954e+00 6.708336181638869800e-01 +3.272573882698126546e-01 2.771858331744477821e-01 3.650874191969583293e+00 8.313793690203402642e-01 4.721314279438468375e+00 1.073305358961462286e+00 5.882774132732964567e+00 2.199287336285580263e+00 3.798152586004966969e-01 5.333129895506141249e-01 +2.307814819672060480e+00 4.104095493932964800e+00 2.489576320635186413e+00 4.496836765063628727e-01 4.578631912903595946e+00 1.329946843468508222e+00 1.683934112573501896e-01 7.002919109156724442e-02 2.471193668340454508e+00 5.351430311502515247e-01 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pickle_blacklist --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pickle_blacklist Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,4 @@ +cos +system +(S'ls ~' +tR. \ No newline at end of file diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline01 Binary file test-data/pipeline01 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline02 Binary file test-data/pipeline02 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline03 Binary file test-data/pipeline03 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline04 Binary file test-data/pipeline04 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline05 Binary file test-data/pipeline05 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline06 Binary file test-data/pipeline06 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline07 Binary file test-data/pipeline07 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline08 Binary file test-data/pipeline08 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline09 Binary file test-data/pipeline09 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline10 Binary file test-data/pipeline10 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline11 Binary file test-data/pipeline11 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline12 Binary file test-data/pipeline12 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline14 Binary file test-data/pipeline14 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline15 Binary file test-data/pipeline15 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline16 Binary file test-data/pipeline16 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline17 Binary file test-data/pipeline17 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline_params05.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pipeline_params05.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,18 @@ + Parameter Value +@ bootstrap bootstrap: True +@ criterion criterion: 'mse' +@ max_depth max_depth: None +@ max_features max_features: 'auto' +@ max_leaf_nodes max_leaf_nodes: None +@ min_impurity_decrease min_impurity_decrease: 0.0 +@ min_impurity_split min_impurity_split: None +@ min_samples_leaf min_samples_leaf: 1 +@ min_samples_split min_samples_split: 2 +@ min_weight_fraction_leaf min_weight_fraction_leaf: 0.0 +@ n_estimators n_estimators: 100 +* n_jobs n_jobs: 1 +@ oob_score oob_score: False +@ random_state random_state: 42 +@ verbose verbose: 0 +@ warm_start warm_start: False + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/pipeline_params18 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pipeline_params18 Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,89 @@ + Parameter Value +* memory memory: None +@ powertransformer powertransformer: PowerTransformer(copy=True, method='yeo-johnson', standardize=True) +* steps "steps: [('powertransformer', PowerTransformer(copy=True, method='yeo-johnson', standardize=True)), ('transformedtargetregressor', TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None, + regressor=RandomForestRegressor(bootstrap=True, + criterion='mse', + max_depth=None, + max_features='auto', + max_leaf_nodes=None, + min_impurity_decrease=0.0, + min_impurity_split=None, + min_samples_leaf=1, + min_samples_split=2, + min_weight_fraction_leaf=0.0, + n_estimators='warn', + n_jobs=1, + oob_score=False, + random_state=10, + verbose=0, + warm_start=False), + transformer=QuantileTransformer(copy=True, + ignore_implicit_zeros=False, + n_quantiles=1000, + output_distribution='uniform', + random_state=10, + subsample=100000)))]" +@ transformedtargetregressor "transformedtargetregressor: TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None, + regressor=RandomForestRegressor(bootstrap=True, + criterion='mse', + max_depth=None, + max_features='auto', + max_leaf_nodes=None, + min_impurity_decrease=0.0, + min_impurity_split=None, + min_samples_leaf=1, + min_samples_split=2, + min_weight_fraction_leaf=0.0, + n_estimators='warn', + n_jobs=1, + oob_score=False, + random_state=10, + verbose=0, + warm_start=False), + transformer=QuantileTransformer(copy=True, + ignore_implicit_zeros=False, + n_quantiles=1000, + output_distribution='uniform', + random_state=10, + subsample=100000))" +@ verbose verbose: False +@ powertransformer__copy powertransformer__copy: True +@ powertransformer__method powertransformer__method: 'yeo-johnson' +@ powertransformer__standardize powertransformer__standardize: True +@ transformedtargetregressor__check_inverse transformedtargetregressor__check_inverse: True +@ transformedtargetregressor__func transformedtargetregressor__func: None +@ transformedtargetregressor__inverse_func transformedtargetregressor__inverse_func: None +@ transformedtargetregressor__regressor "transformedtargetregressor__regressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, + max_features='auto', max_leaf_nodes=None, + min_impurity_decrease=0.0, min_impurity_split=None, + min_samples_leaf=1, min_samples_split=2, + min_weight_fraction_leaf=0.0, n_estimators='warn', + n_jobs=1, oob_score=False, random_state=10, verbose=0, + warm_start=False)" +@ transformedtargetregressor__regressor__bootstrap transformedtargetregressor__regressor__bootstrap: True +@ transformedtargetregressor__regressor__criterion transformedtargetregressor__regressor__criterion: 'mse' +@ transformedtargetregressor__regressor__max_depth transformedtargetregressor__regressor__max_depth: None +@ transformedtargetregressor__regressor__max_features transformedtargetregressor__regressor__max_features: 'auto' +@ transformedtargetregressor__regressor__max_leaf_nodes transformedtargetregressor__regressor__max_leaf_nodes: None +@ transformedtargetregressor__regressor__min_impurity_decrease transformedtargetregressor__regressor__min_impurity_decrease: 0.0 +@ transformedtargetregressor__regressor__min_impurity_split transformedtargetregressor__regressor__min_impurity_split: None +@ transformedtargetregressor__regressor__min_samples_leaf transformedtargetregressor__regressor__min_samples_leaf: 1 +@ transformedtargetregressor__regressor__min_samples_split transformedtargetregressor__regressor__min_samples_split: 2 +@ transformedtargetregressor__regressor__min_weight_fraction_leaf transformedtargetregressor__regressor__min_weight_fraction_leaf: 0.0 +@ transformedtargetregressor__regressor__n_estimators transformedtargetregressor__regressor__n_estimators: 'warn' +* transformedtargetregressor__regressor__n_jobs transformedtargetregressor__regressor__n_jobs: 1 +@ transformedtargetregressor__regressor__oob_score transformedtargetregressor__regressor__oob_score: False +@ transformedtargetregressor__regressor__random_state transformedtargetregressor__regressor__random_state: 10 +@ transformedtargetregressor__regressor__verbose transformedtargetregressor__regressor__verbose: 0 +@ transformedtargetregressor__regressor__warm_start transformedtargetregressor__regressor__warm_start: False +@ transformedtargetregressor__transformer "transformedtargetregressor__transformer: QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000, + output_distribution='uniform', random_state=10, + subsample=100000)" +@ transformedtargetregressor__transformer__copy transformedtargetregressor__transformer__copy: True +@ transformedtargetregressor__transformer__ignore_implicit_zeros transformedtargetregressor__transformer__ignore_implicit_zeros: False +@ transformedtargetregressor__transformer__n_quantiles transformedtargetregressor__transformer__n_quantiles: 1000 +@ transformedtargetregressor__transformer__output_distribution transformedtargetregressor__transformer__output_distribution: 'uniform' +@ transformedtargetregressor__transformer__random_state transformedtargetregressor__transformer__random_state: 10 +@ transformedtargetregressor__transformer__subsample transformedtargetregressor__transformer__subsample: 100000 + Note: @, params eligible for search in searchcv tool. diff -r 000000000000 -r 3b6ee54eb7e2 test-data/precision_recall_curve.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/precision_recall_curve.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2 @@ +precision_recall_curve : +(array([1., 1.]), array([1., 0.]), array([1])) diff -r 000000000000 -r 3b6ee54eb7e2 test-data/precision_recall_fscore_support.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/precision_recall_fscore_support.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2 @@ +precision_recall_fscore_support : +(0.8461538461538461, 0.8461538461538461, 0.8461538461538461, None) diff -r 000000000000 -r 3b6ee54eb7e2 test-data/precision_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/precision_score.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2 @@ +precision_score : +0.8461538461538461 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/predicted_header.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/predicted_header.tabular Sat May 01 00:57:35 2021 +0000 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test-data/prp_model09 Binary file test-data/prp_model09 has changed diff -r 000000000000 -r 3b6ee54eb7e2 test-data/prp_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/prp_result01 Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,15 @@ +0.34079224150348947 -0.3921227933085925 0.2490507280911941 -0.7698156258582727 -0.1701382209728926 +-0.8620757555306106 -0.19048588419219253 0.24710543975009408 0.7422931346186274 -0.6790706051909926 +-0.44857543757211044 0.19920312300180737 -0.812112096739406 0.2785593090771274 0.04069143168750737 +1.3342816328356895 1.6641608262566074 -3.000113357933606 -0.6701123839490727 -0.07045038775469255 +0.7615267260378895 0.9176274108888074 -1.954493327131406 -0.5675301168878727 0.10063563654750733 +0.3517077819346894 0.6351202511326074 -1.518915029366606 -0.30971697444707263 0.09957030020130735 +-1.1546995581165105 -0.5289323469785927 0.7279548225941941 0.8261855855227276 -0.6127421735668926 +-0.17683671467671042 -1.5830256329757926 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0.937773514338 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/y_score.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/y_score.tabular Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,75 @@ +0.04521016253284027 +-0.0017878318955413253 +-0.3380009790698638 +-0.15416229901482092 +-0.008989122568787922 +0.3775746361984437 +-0.20342288788672414 +0.21787658306027935 +-0.5322523189136876 +-0.6361907868807346 +-0.036875765955103335 +-0.24857077769453662 +-0.5305978020035378 +-0.5288479779433272 +-0.22579627342382325 +0.4905346629557697 +-0.12238193946346121 +-0.42773421293023084 +0.16878080982659216 +0.051637548704625946 +0.023623352380110763 +-0.3553978552068183 +-0.4597636722184091 +-0.36924223816393 +-0.539585171546133 +-0.4138055622986405 +-0.25401950905817183 +0.35124248378117207 +-0.5767911246317095 +-0.4452974937020068 +0.13456824841567622 +-0.08366761511503285 +-0.5855411774730717 +0.4493951821813167 +-0.0008118901312900162 +-0.375188782981553 +-0.052180286682808386 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@@ +0 +1 +0 +0 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 +0 +1 +0 +1 +1 +0 +0 +0 +0 +0 +0 +0 +0 +1 +0 +0 +1 +1 +0 +1 +0 +0 +1 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +0 +1 +0 +0 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +0 +0 +0 +1 +1 +0 +1 +0 +0 +0 +1 diff -r 000000000000 -r 3b6ee54eb7e2 test-data/zero_one_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/zero_one_loss.txt Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,2 @@ +zero_one_loss : +0.15384615384615385 diff -r 000000000000 -r 3b6ee54eb7e2 to_categorical.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/to_categorical.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,52 @@ +import argparse +import json +import warnings + +import numpy as np +import pandas as pd +from keras.utils import to_categorical + + +def main(inputs, infile, outfile, num_classes=None): + """ + Parameter + --------- + input : str + File path to galaxy tool parameter + + infile : str + File paths of input vector + + outfile : str + File path to output matrix + + num_classes : str + Total number of classes. If None, this would be inferred as the (largest number in y) + 1 + + """ + warnings.simplefilter("ignore") + + with open(inputs, "r") as param_handler: + params = json.load(param_handler) + + input_header = params["header0"] + header = "infer" if input_header else None + + input_vector = pd.read_csv(infile, sep="\t", header=header) + + output_matrix = to_categorical(input_vector, num_classes=num_classes) + + np.savetxt(outfile, output_matrix, fmt="%d", delimiter="\t") + + +if __name__ == "__main__": + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-y", "--infile", dest="infile") + aparser.add_argument( + "-n", "--num_classes", dest="num_classes", type=int, default=None + ) + aparser.add_argument("-o", "--outfile", dest="outfile") + args = aparser.parse_args() + + main(args.inputs, args.infile, args.outfile, args.num_classes) diff -r 000000000000 -r 3b6ee54eb7e2 train_test_eval.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/train_test_eval.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,477 @@ +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.model_validations import train_test_split +from galaxy_ml.utils import (get_module, get_scoring, load_model, + read_columns, SafeEval, try_get_attr) +from scipy.io import mmread +from sklearn import pipeline +from sklearn.metrics.scorer import _check_multimetric_scoring +from sklearn.model_selection import _search, _validation +from sklearn.model_selection._validation import _score +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 main( + inputs, + infile_estimator, + infile1, + infile2, + outfile_result, + outfile_object=None, + outfile_weights=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 + + 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) + + # 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 + + # handle memory + memory = joblib.Memory(location=CACHE_DIR, verbose=0) + # cache iraps_core fits could increase search speed significantly + if estimator.__class__.__name__ == "IRAPSClassifier": + estimator.set_params(memory=memory) + else: + # For iraps buried in pipeline + new_params = {} + for p, v in estimator_params.items(): + if p.endswith("memory"): + # for case of `__irapsclassifier__memory` + if len(p) > 8 and p[:-8].endswith("irapsclassifier"): + # cache iraps_core fits could increase search + # speed significantly + new_params[p] = memory + # security reason, we don't want memory being + # modified unexpectedly + elif v: + new_params[p] = None + # handle n_jobs + elif p.endswith("n_jobs"): + # For now, 1 CPU is suggested for iprasclassifier + if len(p) > 8 and p[:-8].endswith("irapsclassifier"): + new_params[p] = 1 + else: + new_params[p] = N_JOBS + # for security reason, types of callback are limited + elif p.endswith("callbacks"): + for cb in v: + cb_type = cb["callback_selection"]["callback_type"] + if cb_type not in ALLOWED_CALLBACKS: + raise ValueError("Prohibited callback type: %s!" % cb_type) + + estimator.set_params(**new_params) + + # handle scorer, convert to scorer dict + # Check if scoring is specified + scoring = params["experiment_schemes"]["metrics"].get("scoring", None) + 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"): + 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_result, sep="\t", header=True, index=False) + + memory.clear(warn=False) + + if outfile_object: + main_est = estimator + if isinstance(estimator, pipeline.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) + if getattr(main_est, "model_", None): + del main_est.model_ + if getattr(main_est, "fit_params", None): + del main_est.fit_params + if getattr(main_est, "model_class_", None): + 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("-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, + groups=args.groups, + ref_seq=args.ref_seq, + intervals=args.intervals, + targets=args.targets, + fasta_path=args.fasta_path, + ) diff -r 000000000000 -r 3b6ee54eb7e2 train_test_split.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/train_test_split.py Sat May 01 00:57:35 2021 +0000 @@ -0,0 +1,177 @@ +import argparse +import json +import warnings + +import pandas as pd +from galaxy_ml.model_validations import train_test_split +from galaxy_ml.utils import get_cv, read_columns + + +def _get_single_cv_split(params, array, infile_labels=None, infile_groups=None): + """output (train, test) subset from a cv splitter + + Parameters + ---------- + params : dict + Galaxy tool inputs + array : pandas DataFrame object + The target dataset to split + infile_labels : str + File path to dataset containing target values + infile_groups : str + File path to dataset containing group values + """ + y = None + groups = None + + nth_split = params["mode_selection"]["nth_split"] + + # read groups + if infile_groups: + header = ( + "infer" + if (params["mode_selection"]["cv_selector"]["groups_selector"]["header_g"]) + else None + ) + column_option = params["mode_selection"]["cv_selector"]["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 = params["mode_selection"]["cv_selector"]["groups_selector"][ + "column_selector_options_g" + ]["col_g"] + else: + c = None + + groups = read_columns( + infile_groups, + c=c, + c_option=column_option, + sep="\t", + header=header, + parse_dates=True, + ) + groups = groups.ravel() + + params["mode_selection"]["cv_selector"]["groups_selector"] = groups + + # read labels + if infile_labels: + target_input = params["mode_selection"]["cv_selector"].pop("target_input") + header = "infer" if target_input["header1"] else None + col_index = target_input["col"][0] - 1 + df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) + y = df.iloc[:, col_index].values + + # construct the cv splitter object + splitter, groups = get_cv(params["mode_selection"]["cv_selector"]) + + total_n_splits = splitter.get_n_splits(array.values, y=y, groups=groups) + if nth_split > total_n_splits: + raise ValueError( + "Total number of splits is {}, but got `nth_split` " + "= {}".format(total_n_splits, nth_split) + ) + + i = 1 + for train_index, test_index in splitter.split(array.values, y=y, groups=groups): + # suppose nth_split >= 1 + if i == nth_split: + break + else: + i += 1 + + train = array.iloc[train_index, :] + test = array.iloc[test_index, :] + + return train, test + + +def main( + inputs, + infile_array, + outfile_train, + outfile_test, + infile_labels=None, + infile_groups=None, +): + """ + Parameter + --------- + inputs : str + File path to galaxy tool parameter + + infile_array : str + File paths of input arrays separated by comma + + infile_labels : str + File path to dataset containing labels + + infile_groups : str + File path to dataset containing groups + + outfile_train : str + File path to dataset containing train split + + outfile_test : str + File path to dataset containing test split + """ + warnings.simplefilter("ignore") + + with open(inputs, "r") as param_handler: + params = json.load(param_handler) + + input_header = params["header0"] + header = "infer" if input_header else None + array = pd.read_csv(infile_array, sep="\t", header=header, parse_dates=True) + + # train test split + if params["mode_selection"]["selected_mode"] == "train_test_split": + options = params["mode_selection"]["options"] + shuffle_selection = options.pop("shuffle_selection") + options["shuffle"] = shuffle_selection["shuffle"] + if infile_labels: + header = "infer" if shuffle_selection["header1"] else None + col_index = shuffle_selection["col"][0] - 1 + df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) + labels = df.iloc[:, col_index].values + options["labels"] = labels + + train, test = train_test_split(array, **options) + + # cv splitter + else: + train, test = _get_single_cv_split( + params, array, infile_labels=infile_labels, infile_groups=infile_groups + ) + + print("Input shape: %s" % repr(array.shape)) + print("Train shape: %s" % repr(train.shape)) + print("Test shape: %s" % repr(test.shape)) + train.to_csv(outfile_train, sep="\t", header=input_header, index=False) + test.to_csv(outfile_test, sep="\t", header=input_header, index=False) + + +if __name__ == "__main__": + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-X", "--infile_array", dest="infile_array") + aparser.add_argument("-y", "--infile_labels", dest="infile_labels") + aparser.add_argument("-g", "--infile_groups", dest="infile_groups") + aparser.add_argument("-o", "--outfile_train", dest="outfile_train") + aparser.add_argument("-t", "--outfile_test", dest="outfile_test") + args = aparser.parse_args() + + main( + args.inputs, + args.infile_array, + args.outfile_train, + args.outfile_test, + args.infile_labels, + args.infile_groups, + )