# HG changeset patch
# User bgruening
# Date 1572643619 14400
# Node ID 0985b0dd6f1a3462b6a344bcb21c0e1cad8617fb
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
diff -r 000000000000 -r 0985b0dd6f1a README.rst
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.rst Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a fitted_model_eval.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/fitted_model_eval.py Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,160 @@
+import argparse
+import json
+import pandas as pd
+import warnings
+
+from scipy.io import mmread
+from sklearn.pipeline import Pipeline
+from sklearn.metrics.scorer import _check_multimetric_scoring
+from sklearn.model_selection._validation import _score
+from galaxy_ml.utils import get_scoring, load_model, read_columns
+
+
+def _get_X_y(params, infile1, infile2):
+ """ read from inputs and output X and y
+
+ Parameters
+ ----------
+ params : dict
+ Tool inputs parameter
+ infile1 : str
+ File path to dataset containing features
+ infile2 : str
+ File path to dataset containing target values
+
+ """
+ # store read dataframe object
+ loaded_df = {}
+
+ input_type = params['input_options']['selected_input']
+ # tabular input
+ if input_type == 'tabular':
+ header = 'infer' if params['input_options']['header1'] else None
+ column_option = (params['input_options']['column_selector_options_1']
+ ['selected_column_selector_option'])
+ if column_option in ['by_index_number', 'all_but_by_index_number',
+ 'by_header_name', 'all_but_by_header_name']:
+ c = params['input_options']['column_selector_options_1']['col1']
+ else:
+ c = None
+
+ df_key = infile1 + repr(header)
+ df = pd.read_csv(infile1, sep='\t', header=header,
+ parse_dates=True)
+ loaded_df[df_key] = df
+
+ X = read_columns(df, c=c, c_option=column_option).astype(float)
+ # sparse input
+ elif input_type == 'sparse':
+ X = mmread(open(infile1, 'r'))
+
+ # Get target y
+ header = 'infer' if params['input_options']['header2'] else None
+ column_option = (params['input_options']['column_selector_options_2']
+ ['selected_column_selector_option2'])
+ if column_option in ['by_index_number', 'all_but_by_index_number',
+ 'by_header_name', 'all_but_by_header_name']:
+ c = params['input_options']['column_selector_options_2']['col2']
+ else:
+ c = None
+
+ df_key = infile2 + repr(header)
+ if df_key in loaded_df:
+ infile2 = loaded_df[df_key]
+ else:
+ infile2 = pd.read_csv(infile2, sep='\t',
+ header=header, parse_dates=True)
+ loaded_df[df_key] = infile2
+
+ y = read_columns(
+ infile2,
+ c=c,
+ c_option=column_option,
+ sep='\t',
+ header=header,
+ parse_dates=True)
+ if len(y.shape) == 2 and y.shape[1] == 1:
+ y = y.ravel()
+
+ return X, y
+
+
+def main(inputs, infile_estimator, outfile_eval,
+ infile_weights=None, infile1=None,
+ infile2=None):
+ """
+ Parameter
+ ---------
+ inputs : str
+ File path to galaxy tool parameter
+
+ infile_estimator : strgit
+ File path to trained estimator input
+
+ outfile_eval : str
+ File path to save the evalulation results, tabular
+
+ infile_weights : str
+ File path to weights input
+
+ infile1 : str
+ File path to dataset containing features
+
+ infile2 : str
+ File path to dataset containing target values
+ """
+ warnings.filterwarnings('ignore')
+
+ with open(inputs, 'r') as param_handler:
+ params = json.load(param_handler)
+
+ X_test, y_test = _get_X_y(params, infile1, infile2)
+
+ # load model
+ with open(infile_estimator, 'rb') as est_handler:
+ estimator = load_model(est_handler)
+
+ main_est = estimator
+ if isinstance(estimator, Pipeline):
+ main_est = estimator.steps[-1][-1]
+ if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
+ if not infile_weights or infile_weights == 'None':
+ raise ValueError("The selected model skeleton asks for weights, "
+ "but no dataset for weights was provided!")
+ main_est.load_weights(infile_weights)
+
+ # handle scorer, convert to scorer dict
+ scoring = params['scoring']
+ scorer = get_scoring(scoring)
+ scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+
+ if hasattr(estimator, 'evaluate'):
+ scores = estimator.evaluate(X_test, y_test=y_test,
+ scorer=scorer,
+ is_multimetric=True)
+ else:
+ scores = _score(estimator, X_test, y_test, scorer,
+ is_multimetric=True)
+
+ # handle output
+ for name, score in scores.items():
+ scores[name] = [score]
+ df = pd.DataFrame(scores)
+ df = df[sorted(df.columns)]
+ df.to_csv(path_or_buf=outfile_eval, sep='\t',
+ header=True, index=False)
+
+
+if __name__ == '__main__':
+ aparser = argparse.ArgumentParser()
+ aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+ aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
+ aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
+ aparser.add_argument("-X", "--infile1", dest="infile1")
+ aparser.add_argument("-y", "--infile2", dest="infile2")
+ aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
+ args = aparser.parse_args()
+
+ main(args.inputs, args.infile_estimator, args.outfile_eval,
+ infile_weights=args.infile_weights, infile1=args.infile1,
+ infile2=args.infile2)
diff -r 000000000000 -r 0985b0dd6f1a keras_deep_learning.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_deep_learning.py Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,370 @@
+import argparse
+import json
+import keras
+import pandas as pd
+import pickle
+import six
+import warnings
+
+from ast import literal_eval
+from keras.models import Sequential, Model
+from galaxy_ml.utils import try_get_attr, get_search_params, SafeEval
+
+
+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.
+ if l2 is None:
+ l2 = 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 == '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 == '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')
+ 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:
+ f.write(json_string)
+
+
+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']).split(',')
+ 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 0985b0dd6f1a keras_macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_macros.xml Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,985 @@
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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 0985b0dd6f1a main_macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/main_macros.xml Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,1997 @@
+
+ 1.0.7.12
+
+ 0.2.0
+
+
+
+ python
+ 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 0985b0dd6f1a ml_visualization_ex.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/ml_visualization_ex.py Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,306 @@
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import plotly
+import plotly.graph_objs as go
+import warnings
+
+from keras.models import model_from_json
+from keras.utils import plot_model
+from sklearn.feature_selection.base import SelectorMixin
+from sklearn.metrics import precision_recall_curve, average_precision_score
+from sklearn.metrics import roc_curve, auc
+from sklearn.pipeline import Pipeline
+from galaxy_ml.utils import load_model, read_columns, SafeEval
+
+
+safe_eval = SafeEval()
+
+
+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):
+ """
+ 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
+ """
+ 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']
+ 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)
+
+ elif plot_type == 'pr_curve':
+ df1 = pd.read_csv(infile1, sep='\t', header=None)
+ df2 = pd.read_csv(infile2, sep='\t', header=None)
+
+ precision = {}
+ recall = {}
+ ap = {}
+
+ pos_label = params['plotting_selection']['pos_label'].strip() \
+ or None
+ for col in df1.columns:
+ y_true = df1[col].values
+ y_score = df2[col].values
+
+ precision[col], recall[col], _ = precision_recall_curve(
+ y_true, y_score, pos_label=pos_label)
+ ap[col] = average_precision_score(
+ y_true, y_score, pos_label=pos_label or 1)
+
+ if len(df1.columns) > 1:
+ precision["micro"], recall["micro"], _ = precision_recall_curve(
+ df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
+ ap['micro'] = average_precision_score(
+ df1.values, df2.values, average='micro',
+ pos_label=pos_label or 1)
+
+ data = []
+ for key in precision.keys():
+ trace = go.Scatter(
+ x=recall[key],
+ y=precision[key],
+ mode='lines',
+ name='%s (area = %.2f)' % (key, ap[key]) if key == 'micro'
+ else 'column %s (area = %.2f)' % (key, ap[key])
+ )
+ data.append(trace)
+
+ layout = go.Layout(
+ title=title or "Precision-Recall curve",
+ xaxis=dict(title='Recall'),
+ yaxis=dict(title='Precision')
+ )
+
+ fig = go.Figure(data=data, layout=layout)
+
+ elif plot_type == 'roc_curve':
+ df1 = pd.read_csv(infile1, sep='\t', header=None)
+ df2 = pd.read_csv(infile2, sep='\t', header=None)
+
+ fpr = {}
+ tpr = {}
+ roc_auc = {}
+
+ pos_label = params['plotting_selection']['pos_label'].strip() \
+ or None
+ for col in df1.columns:
+ y_true = df1[col].values
+ y_score = df2[col].values
+
+ fpr[col], tpr[col], _ = roc_curve(
+ y_true, y_score, pos_label=pos_label)
+ roc_auc[col] = auc(fpr[col], tpr[col])
+
+ if len(df1.columns) > 1:
+ fpr["micro"], tpr["micro"], _ = roc_curve(
+ df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
+ roc_auc['micro'] = auc(fpr["micro"], tpr["micro"])
+
+ data = []
+ for key in fpr.keys():
+ trace = go.Scatter(
+ x=fpr[key],
+ y=tpr[key],
+ mode='lines',
+ name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro'
+ else 'column %s (area = %.2f)' % (key, roc_auc[key])
+ )
+ data.append(trace)
+
+ trace = go.Scatter(x=[0, 1], y=[0, 1],
+ mode='lines',
+ line=dict(color='black', dash='dash'),
+ showlegend=False)
+ data.append(trace)
+
+ layout = go.Layout(
+ title=title or "Receiver operating characteristic curve",
+ xaxis=dict(title='False Positive Rate'),
+ yaxis=dict(title='True Positive Rate')
+ )
+
+ fig = go.Figure(data=data, layout=layout)
+
+ 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=title or None
+ )
+
+ fig = go.Figure(data=[data], layout=layout)
+
+ 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'
+ ),
+ title=title or 'Learning Curve'
+ )
+ fig = go.Figure(data=[data1, data2], layout=layout)
+
+ 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")
+ __import__('os').rename('output.png', 'output')
+
+ return 0
+
+ plotly.offline.plot(fig, filename="output.html",
+ auto_open=False)
+ # to be discovered by `from_work_dir`
+ __import__('os').rename('output.html', 'output')
+
+
+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")
+ 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)
diff -r 000000000000 -r 0985b0dd6f1a model_prediction.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/model_prediction.py Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,262 @@
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import tabix
+import warnings
+
+from scipy.io import mmread
+from sklearn.pipeline import Pipeline
+
+from galaxy_ml.externals.selene_sdk.sequences import Genome
+from galaxy_ml.utils import (load_model, read_columns,
+ get_module, try_get_attr)
+
+
+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.fit()
+
+ variants = pred_data_generator.variants
+ # TODO : remove the following block after galaxy-ml v0.7.13
+ blacklist_tabix = getattr(pred_data_generator.reference_genome_,
+ '_blacklist_tabix', None)
+ clean_variants = []
+ if blacklist_tabix:
+ start_radius = pred_data_generator.start_radius_
+ end_radius = pred_data_generator.end_radius_
+
+ for chrom, pos, name, ref, alt, strand in variants:
+ center = pos + len(ref) // 2
+ start = center - start_radius
+ end = center + end_radius
+
+ if isinstance(pred_data_generator.reference_genome_, Genome):
+ if "chr" not in chrom:
+ chrom = "chr" + chrom
+ if "MT" in chrom:
+ chrom = chrom[:-1]
+ try:
+ rows = blacklist_tabix.query(chrom, start, end)
+ found = 0
+ for row in rows:
+ found = 1
+ break
+ if found:
+ continue
+ except tabix.TabixError:
+ pass
+
+ clean_variants.append((chrom, pos, name, ref, alt, strand))
+ else:
+ clean_variants = variants
+
+ setattr(pred_data_generator, 'variants', clean_variants)
+
+ variants = np.array(clean_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 0985b0dd6f1a search_model_validation.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/search_model_validation.py Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,581 @@
+import argparse
+import collections
+import imblearn
+import joblib
+import json
+import numpy as np
+import pandas as pd
+import pickle
+import skrebate
+import sklearn
+import sys
+import xgboost
+import warnings
+from imblearn import under_sampling, over_sampling, combine
+from scipy.io import mmread
+from mlxtend import classifier, regressor
+from sklearn.base import clone
+from sklearn import (cluster, compose, decomposition, ensemble,
+ feature_extraction, feature_selection,
+ gaussian_process, kernel_approximation, metrics,
+ model_selection, naive_bayes, neighbors,
+ pipeline, preprocessing, svm, linear_model,
+ tree, discriminant_analysis)
+from sklearn.exceptions import FitFailedWarning
+from sklearn.model_selection._validation import _score, cross_validate
+from sklearn.model_selection import _search, _validation
+
+from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
+ read_columns, try_get_attr, get_module)
+
+
+_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(__import__('os').environ.get('GALAXY_SLOTS', 1))
+CACHE_DIR = './cached'
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
+ 'nthread', 'callbacks')
+ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
+ 'CSVLogger', 'None')
+
+
+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 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')
+
+ with open(inputs, 'r') as param_handler:
+ params = json.load(param_handler)
+
+ # conflict param checker
+ if params['outer_split']['split_mode'] == 'nested_cv' \
+ and params['save'] != 'nope':
+ raise ValueError("Save best estimator is not possible for nested CV!")
+
+ if not (params['search_schemes']['options']['refit']) \
+ and params['save'] != 'nope':
+ raise ValueError("Save best estimator is not possible when refit "
+ "is False!")
+
+ params_builder = params['search_schemes']['search_params_builder']
+
+ with open(infile_estimator, 'rb') as estimator_handler:
+ estimator = load_model(estimator_handler)
+ 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
+
+ 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)
+ 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()
+ options['cv_selector']['groups_selector'] = groups
+
+ splitter, groups = get_cv(options.pop('cv_selector'))
+ options['cv'] = splitter
+ options['n_jobs'] = N_JOBS
+ primary_scoring = options['scoring']['primary_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
+
+ # 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
+ 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}
+ estimator.set_params(**new_params)
+ # security reason, we don't want memory being
+ # modified unexpectedly
+ elif v:
+ new_params = {p, None}
+ estimator.set_params(**new_params)
+ # For now, 1 CPU is suggested for iprasclassifier
+ elif p.endswith('n_jobs'):
+ new_params = {p: 1}
+ estimator.set_params(**new_params)
+ # for security reason, types of callbacks 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)
+
+ param_grid = _eval_search_params(params_builder)
+ searcher = optimizer(estimator, param_grid, **options)
+
+ # do nested split
+ split_mode = params['outer_split'].pop('split_mode')
+ # nested CV, outer cv using cross_validate
+ if split_mode == 'nested_cv':
+ outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
+
+ if options['error_score'] == 'raise':
+ rval = cross_validate(
+ searcher, X, y, scoring=options['scoring'],
+ cv=outer_cv, n_jobs=N_JOBS, verbose=0,
+ error_score=options['error_score'])
+ 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=0,
+ error_score=options['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_result, sep='\t',
+ header=True, index=False)
+ else:
+ if split_mode == 'train_test_split':
+ train_test_split = try_get_attr(
+ 'galaxy_ml.model_validations', 'train_test_split')
+ # 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)
+ 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)
+ # end train_test_split
+
+ # shared by both train_test_split and non-split
+ 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))
+
+ # no outer split
+ if split_mode == 'no':
+ # save results
+ 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)
+
+ # train_test_split, output test result using best_estimator_
+ # or rebuild the trained estimator using weights if applicable.
+ else:
+ scorer_ = searcher.scorer_
+ if isinstance(scorer_, collections.Mapping):
+ is_multimetric = True
+ else:
+ is_multimetric = False
+
+ best_estimator_ = getattr(searcher, 'best_estimator_', None)
+ if not best_estimator_:
+ raise ValueError("GridSearchCV object has no "
+ "`best_estimator_` when `refit`=False!")
+
+ if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier' \
+ and hasattr(estimator.data_batch_generator, 'target_path'):
+ 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_result, sep='\t',
+ header=True, index=False)
+
+ memory.clear(warn=False)
+
+ 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
+
+ main_est = best_estimator_
+ if isinstance(best_estimator_, pipeline.Pipeline):
+ main_est = best_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_
+ 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(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 0985b0dd6f1a simple_model_fit.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/simple_model_fit.py Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,145 @@
+import argparse
+import json
+import pandas as pd
+import pickle
+
+from galaxy_ml.utils import load_model, read_columns
+from sklearn.pipeline import Pipeline
+
+
+def _get_X_y(params, infile1, infile2):
+ """ read from inputs and output X and y
+
+ Parameters
+ ----------
+ params : dict
+ Tool inputs parameter
+ infile1 : str
+ File path to dataset containing features
+ infile2 : str
+ File path to dataset containing target values
+
+ """
+ # store read dataframe object
+ loaded_df = {}
+
+ input_type = params['input_options']['selected_input']
+ # tabular input
+ if input_type == 'tabular':
+ header = 'infer' if params['input_options']['header1'] else None
+ column_option = (params['input_options']['column_selector_options_1']
+ ['selected_column_selector_option'])
+ if column_option in ['by_index_number', 'all_but_by_index_number',
+ 'by_header_name', 'all_but_by_header_name']:
+ c = params['input_options']['column_selector_options_1']['col1']
+ else:
+ c = None
+
+ df_key = infile1 + repr(header)
+ df = pd.read_csv(infile1, sep='\t', header=header,
+ parse_dates=True)
+ loaded_df[df_key] = df
+
+ X = read_columns(df, c=c, c_option=column_option).astype(float)
+ # sparse input
+ elif input_type == 'sparse':
+ X = mmread(open(infile1, 'r'))
+
+ # Get target y
+ header = 'infer' if params['input_options']['header2'] else None
+ column_option = (params['input_options']['column_selector_options_2']
+ ['selected_column_selector_option2'])
+ if column_option in ['by_index_number', 'all_but_by_index_number',
+ 'by_header_name', 'all_but_by_header_name']:
+ c = params['input_options']['column_selector_options_2']['col2']
+ else:
+ c = None
+
+ df_key = infile2 + repr(header)
+ if df_key in loaded_df:
+ infile2 = loaded_df[df_key]
+ else:
+ infile2 = pd.read_csv(infile2, sep='\t',
+ header=header, parse_dates=True)
+ loaded_df[df_key] = infile2
+
+ y = read_columns(
+ infile2,
+ c=c,
+ c_option=column_option,
+ sep='\t',
+ header=header,
+ parse_dates=True)
+ if len(y.shape) == 2 and y.shape[1] == 1:
+ y = y.ravel()
+
+ return X, y
+
+
+def main(inputs, infile_estimator, infile1, infile2, out_object,
+ out_weights=None):
+ """ main
+
+ Parameters
+ ----------
+ inputs : str
+ File path to galaxy tool parameter
+
+ infile_estimator : str
+ File paths of input estimator
+
+ infile1 : str
+ File path to dataset containing features
+
+ infile2 : str
+ File path to dataset containing target labels
+
+ out_object : str
+ File path for output of fitted model or skeleton
+
+ out_weights : str
+ File path for output of weights
+
+ """
+ with open(inputs, 'r') as param_handler:
+ params = json.load(param_handler)
+
+ # load model
+ with open(infile_estimator, 'rb') as est_handler:
+ estimator = load_model(est_handler)
+
+ X_train, y_train = _get_X_y(params, infile1, infile2)
+
+ estimator.fit(X_train, y_train)
+
+ main_est = estimator
+ if isinstance(main_est, Pipeline):
+ main_est = main_est.steps[-1][-1]
+ if hasattr(main_est, 'model_') \
+ and hasattr(main_est, 'save_weights'):
+ if out_weights:
+ main_est.save_weights(out_weights)
+ del main_est.model_
+ del main_est.fit_params
+ del main_est.model_class_
+ del main_est.validation_data
+ if getattr(main_est, 'data_generator_', None):
+ del main_est.data_generator_
+
+ with open(out_object, 'wb') as output_handler:
+ pickle.dump(estimator, output_handler,
+ pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == '__main__':
+ aparser = argparse.ArgumentParser()
+ aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+ aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator")
+ aparser.add_argument("-y", "--infile1", dest="infile1")
+ aparser.add_argument("-g", "--infile2", dest="infile2")
+ aparser.add_argument("-o", "--out_object", dest="out_object")
+ aparser.add_argument("-t", "--out_weights", dest="out_weights")
+ args = aparser.parse_args()
+
+ main(args.inputs, args.infile_estimator, args.infile1,
+ args.infile2, args.out_object, args.out_weights)
diff -r 000000000000 -r 0985b0dd6f1a stacking_ensembles.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/stacking_ensembles.py Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,132 @@
+import argparse
+import ast
+import json
+import mlxtend.regressor
+import mlxtend.classifier
+import pandas as pd
+import pickle
+import sklearn
+import sys
+import warnings
+from sklearn import ensemble
+
+from galaxy_ml.utils import (load_model, get_cv, get_estimator,
+ get_search_params)
+
+
+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 0985b0dd6f1a test-data/GridSearchCV.zip
Binary file test-data/GridSearchCV.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/LinearRegression01.zip
Binary file test-data/LinearRegression01.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/LinearRegression02.zip
Binary file test-data/LinearRegression02.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/RF01704.fasta
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/RF01704.fasta Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,4 @@
+>CP000097.1/1411351-1411410
+CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA
+>ACNY01000002.1/278641-278580
+GAUCGUUCACUUCGCAUCGCGCGAAGCGCAGUUCGCCUCAGGCCAUGGAACGGGGACCUGAG
diff -r 000000000000 -r 0985b0dd6f1a test-data/RFE.zip
Binary file test-data/RFE.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/RandomForestClassifier.zip
Binary file test-data/RandomForestClassifier.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/RandomForestRegressor01.zip
Binary file test-data/RandomForestRegressor01.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/StackingCVRegressor01.zip
Binary file test-data/StackingCVRegressor01.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/StackingCVRegressor02.zip
Binary file test-data/StackingCVRegressor02.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/StackingRegressor02.zip
Binary file test-data/StackingRegressor02.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/StackingVoting03.zip
Binary file test-data/StackingVoting03.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/XGBRegressor01.zip
Binary file test-data/XGBRegressor01.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/abc_model01
Binary file test-data/abc_model01 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/abc_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/abc_result01 Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/abr_model01
Binary file test-data/abr_model01 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/abr_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/abr_result01 Fri Nov 01 17:26:59 2019 -0400
@@ -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 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.7191695359690001
+61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 1.1503117056799999
+-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -0.7191695359690001
diff -r 000000000000 -r 0985b0dd6f1a test-data/accuracy_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/accuracy_score.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+accuracy_score :
+0.8461538461538461
diff -r 000000000000 -r 0985b0dd6f1a test-data/auc.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/auc.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+auc :
+2.5
diff -r 000000000000 -r 0985b0dd6f1a test-data/average_precision_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/average_precision_score.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+average_precision_score :
+1.0
diff -r 000000000000 -r 0985b0dd6f1a test-data/best_estimator_.zip
Binary file test-data/best_estimator_.zip has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/best_params_.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/best_params_.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,1 @@
+{'estimator__n_estimators': 100}
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/best_score_.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/best_score_.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+best_score_
+0.7976348550293088
diff -r 000000000000 -r 0985b0dd6f1a test-data/blobs.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/blobs.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,101 @@
+0 1 0
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diff -r 000000000000 -r 0985b0dd6f1a test-data/brier_score_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/brier_score_loss.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+brier_score_loss :
+0.5641025641025641
diff -r 000000000000 -r 0985b0dd6f1a test-data/circles.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/circles.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/class.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/class.txt Fri Nov 01 17:26:59 2019 -0400
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diff -r 000000000000 -r 0985b0dd6f1a test-data/classification_report.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/classification_report.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,11 @@
+classification_report :
+ precision recall f1-score support
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diff -r 000000000000 -r 0985b0dd6f1a test-data/cluster_result01.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/cluster_result18.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/cluster_result19.txt
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diff -r 000000000000 -r 0985b0dd6f1a test-data/cluster_result21.txt
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/confusion_matrix.txt Fri Nov 01 17:26:59 2019 -0400
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diff -r 000000000000 -r 0985b0dd6f1a test-data/converter_result01.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result01.json Fri Nov 01 17:26:59 2019 -0400
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diff -r 000000000000 -r 0985b0dd6f1a test-data/converter_result02.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result02.json Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,4 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/csc_sparse1.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_sparse1.mtx Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,9 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/csc_sparse2.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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@@ -0,0 +1,9 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/csc_stack_result01.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/csr_sparse1.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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@@ -0,0 +1,9 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/csr_sparse2.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/csr_stack_result01.mtx
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/deepsear_1feature.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/empty_file.txt
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diff -r 000000000000 -r 0985b0dd6f1a test-data/f1_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result01
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result03
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result03 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result04
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result04 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result05
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result05 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result06
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result06 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result07
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result07 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result08
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result08 Fri Nov 01 17:26:59 2019 -0400
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result09
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result09 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,11 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/feature_selection_result10
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result10 Fri Nov 01 17:26:59 2019 -0400
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/friedman2.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/friedman3.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/gaus.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gaus.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/gbc_model01
Binary file test-data/gbc_model01 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/gbc_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbc_result01 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,6 @@
+0 1 2 3 predicted
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diff -r 000000000000 -r 0985b0dd6f1a test-data/gbr_model01
Binary file test-data/gbr_model01 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/gbr_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbr_prediction_result01.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,88 @@
+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 29 69 68 66.1 63 71 68 57 0 0 0 0 1 0 0 69.58390938468499
+2016 4 27 59 60 60.7 59 65 60 50 0 0 0 0 0 0 1 62.52052253790107
+2016 11 28 53 48 48.0 46 48 49 44 0 1 0 0 0 0 0 51.680887055498296
+2016 10 12 60 62 61.0 60 63 63 52 0 0 0 0 0 0 1 61.246237852679315
+2016 6 19 67 65 70.4 69 73 70 58 0 0 0 1 0 0 0 65.03047512424794
+2016 5 7 68 77 63.0 61 65 63 83 0 0 1 0 0 0 0 71.59883326872612
+2016 7 25 75 80 77.1 75 82 76 81 0 1 0 0 0 0 0 78.5487110206859
+2016 8 15 90 83 76.6 76 79 75 70 0 1 0 0 0 0 0 80.77545676519121
+2016 10 28 58 60 55.6 52 56 55 52 1 0 0 0 0 0 0 62.11231551486949
+2016 6 5 80 81 68.0 64 70 66 54 0 0 0 1 0 0 0 72.42798354934989
+2016 3 19 58 63 54.2 54 59 54 62 0 0 1 0 0 0 0 61.63169537788603
+2016 6 7 92 86 68.3 67 69 70 58 0 0 0 0 0 1 0 74.4731129283374
+2016 12 10 41 36 45.9 44 48 44 65 0 0 1 0 0 0 0 39.39391240070939
+2016 4 23 73 64 59.9 56 63 59 57 0 0 1 0 0 0 0 62.93072314757922
+2016 6 24 75 68 71.5 67 73 73 65 1 0 0 0 0 0 0 73.42248151259705
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+2016 6 28 78 85 72.4 72 76 74 67 0 0 0 0 0 1 0 77.87772901898535
+2016 10 6 63 66 63.3 62 67 63 55 0 0 0 0 1 0 0 64.65839290042257
+2016 2 17 55 56 50.0 45 51 49 46 0 0 0 0 0 0 1 54.26509333618539
+2016 6 15 66 60 69.7 65 73 71 69 0 0 0 0 0 0 1 66.15190585447276
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+2016 5 22 66 59 65.9 62 66 65 80 0 0 0 1 0 0 0 60.46222634728846
+2016 7 15 75 77 76.0 74 80 78 75 1 0 0 0 0 0 0 82.42822449858019
+2016 4 22 81 73 59.7 59 64 60 59 1 0 0 0 0 0 0 72.82325656081416
+2016 4 29 61 64 61.2 61 65 61 49 1 0 0 0 0 0 0 65.00954748796826
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+2016 8 16 83 84 76.5 72 78 78 90 0 0 0 0 0 1 0 82.12928759095375
+2016 8 1 76 73 77.4 76 78 79 65 0 1 0 0 0 0 0 72.22807576891064
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+2016 9 5 67 68 73.5 71 75 73 54 0 1 0 0 0 0 0 68.96231670707674
+2016 12 20 39 46 45.1 45 49 45 62 0 0 0 0 0 1 0 41.12571355242521
+2016 5 1 61 68 61.6 60 65 60 75 0 0 0 1 0 0 0 66.15287588548186
+2016 3 28 59 51 55.5 55 57 55 47 0 1 0 0 0 0 0 59.11011722462772
+2016 4 21 81 81 59.4 55 61 59 55 0 0 0 0 1 0 0 74.41085058157081
+2016 1 6 40 44 46.1 43 49 48 40 0 0 0 0 0 0 1 41.20470505512009
+2016 10 21 58 62 57.8 56 60 59 44 1 0 0 0 0 0 0 61.62578223843827
+2016 5 2 68 77 61.9 60 66 61 59 0 1 0 0 0 0 0 72.48517225879384
+2016 3 1 53 54 51.5 48 56 50 53 0 0 0 0 0 1 0 53.70588500948454
+2016 7 21 78 82 76.8 73 81 78 84 0 0 0 0 1 0 0 82.7108327367616
+2016 3 17 51 53 53.9 49 58 52 62 0 0 0 0 1 0 0 53.251174797156146
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+2016 3 16 49 51 53.7 52 54 55 65 0 0 0 0 0 0 1 54.86875365404632
+2016 4 26 55 59 60.5 56 61 62 75 0 0 0 0 0 1 0 61.34654097192005
+2016 6 10 67 65 68.8 67 71 67 73 1 0 0 0 0 0 0 65.38427016260138
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+2016 3 7 64 60 52.4 49 57 53 71 0 1 0 0 0 0 0 61.08886411894317
+2016 9 18 75 68 70.0 66 73 71 90 0 0 0 1 0 0 0 70.7844532497458
+2016 3 20 63 61 54.3 51 56 55 50 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 0985b0dd6f1a test-data/get_params.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params01.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params02.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params03.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params03.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params04.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params04.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params05.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params05.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,31 @@
+ Parameter Value
+* memory memory: None
+* steps "steps: [('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=100, n_jobs=1,
+ oob_score=False, random_state=42, verbose=0, warm_start=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=100, n_jobs=1,
+ oob_score=False, random_state=42, verbose=0, warm_start=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: 100
+* randomforestregressor__n_jobs randomforestregressor__n_jobs: 1
+@ randomforestregressor__oob_score randomforestregressor__oob_score: False
+@ randomforestregressor__random_state randomforestregressor__random_state: 42
+* randomforestregressor__verbose randomforestregressor__verbose: 0
+@ randomforestregressor__warm_start randomforestregressor__warm_start: False
+ Note: @, searchable params in searchcv too.
diff -r 000000000000 -r 0985b0dd6f1a test-data/get_params06.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params06.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params07.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params07.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params08.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params08.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params09.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params09.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params10.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params10.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params11.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params11.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/get_params12.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params12.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,47 @@
+ Parameter Value
+* memory memory: None
+* steps "steps: [('rfe', RFE(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=None, step=1, verbose=0))]"
+@ rfe "rfe: RFE(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=None, step=1, verbose=0)"
+@ rfe__estimator__base_score rfe__estimator__base_score: 0.5
+@ rfe__estimator__booster rfe__estimator__booster: 'gbtree'
+@ rfe__estimator__colsample_bylevel rfe__estimator__colsample_bylevel: 1
+@ rfe__estimator__colsample_bytree rfe__estimator__colsample_bytree: 1
+@ rfe__estimator__gamma rfe__estimator__gamma: 0
+@ rfe__estimator__learning_rate rfe__estimator__learning_rate: 0.1
+@ rfe__estimator__max_delta_step rfe__estimator__max_delta_step: 0
+@ rfe__estimator__max_depth rfe__estimator__max_depth: 3
+@ rfe__estimator__min_child_weight rfe__estimator__min_child_weight: 1
+@ rfe__estimator__missing rfe__estimator__missing: nan
+@ rfe__estimator__n_estimators rfe__estimator__n_estimators: 100
+* rfe__estimator__n_jobs rfe__estimator__n_jobs: 1
+* rfe__estimator__nthread rfe__estimator__nthread: None
+@ rfe__estimator__objective rfe__estimator__objective: 'reg:linear'
+@ rfe__estimator__random_state rfe__estimator__random_state: 0
+@ rfe__estimator__reg_alpha rfe__estimator__reg_alpha: 0
+@ rfe__estimator__reg_lambda rfe__estimator__reg_lambda: 1
+@ rfe__estimator__scale_pos_weight rfe__estimator__scale_pos_weight: 1
+@ rfe__estimator__seed rfe__estimator__seed: None
+@ rfe__estimator__silent rfe__estimator__silent: True
+@ rfe__estimator__subsample rfe__estimator__subsample: 1
+@ rfe__estimator "rfe__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)"
+@ rfe__n_features_to_select rfe__n_features_to_select: None
+@ rfe__step rfe__step: 1
+* rfe__verbose rfe__verbose: 0
+ Note: @, searchable params in searchcv too.
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model01
Binary file test-data/glm_model01 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model02
Binary file test-data/glm_model02 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model03
Binary file test-data/glm_model03 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model04
Binary file test-data/glm_model04 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model05
Binary file test-data/glm_model05 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model06
Binary file test-data/glm_model06 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model07
Binary file test-data/glm_model07 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_model08
Binary file test-data/glm_model08 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result01 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 3703215242836.872
+91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 3875943636708.156
+-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -2071574726112.0168
+61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 2642119730255.405
+-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -8851040854159.11
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_result02
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result02 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1
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+0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_result03
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result03 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1
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+0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_result04
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result04 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.5282637592226301
+91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5180352211818147
+-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 0.012682414140451959
+61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.1869842234155321
+-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.6599360904302456
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_result05
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result05 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1
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+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 0985b0dd6f1a test-data/glm_result06
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result06 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+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 0985b0dd6f1a test-data/glm_result07
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result07 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.6093152833692663
+91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5963828164943974
+-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.07927429227257943
+61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.2621440442022235
+-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.7330414645145749
diff -r 000000000000 -r 0985b0dd6f1a test-data/glm_result08
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result08 Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
+3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 0
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+0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 000000000000 -r 0985b0dd6f1a test-data/grid_scores_.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/grid_scores_.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,18 @@
+grid_scores_
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diff -r 000000000000 -r 0985b0dd6f1a test-data/hamming_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hamming_loss.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+hamming_loss :
+0.15384615384615385
diff -r 000000000000 -r 0985b0dd6f1a test-data/hastie.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hastie.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,12001 @@
+0 1 2 3 4 5 6 7 8 9 0
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diff -r 000000000000 -r 0985b0dd6f1a test-data/hinge_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/imblearn_X.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/imblearn_y.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/jaccard_similarity_score.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/jaccard_similarity_score.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+jaccard_similarity_score :
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diff -r 000000000000 -r 0985b0dd6f1a test-data/keras01.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras01.json Fri Nov 01 17:26:59 2019 -0400
@@ -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, 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, "activation": "relu"}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 10, "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, "activation": "softmax"}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras02.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras02.json Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,1 @@
+{"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, "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 32, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 1}, "inbound_nodes": [[["embedding_1", 0, 0, {}]]]}, {"name": "dense_1", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "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, "axis": -1}, "inbound_nodes": [[["dense_1", 0, 0, {}], ["aux_input", 0, 0, {}]]]}, {"name": "dense_2", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 64, "activation": "relu", "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": [[["concatenate_1", 0, 0, {}]]]}, {"name": "dense_3", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 64, "activation": "relu", "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_2", 0, 0, {}]]]}, {"name": "dense_4", "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "units": 64, "activation": "relu", "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_3", 0, 0, {}]]]}, {"name": "dense_5", "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "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": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras03.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras03.json Fri Nov 01 17:26:59 2019 -0400
@@ -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 0985b0dd6f1a test-data/keras04.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras04.json Fri Nov 01 17:26:59 2019 -0400
@@ -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": 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, "activation": "linear"}}, {"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": 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, "activation": "linear"}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_batch_model01
Binary file test-data/keras_batch_model01 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_batch_model02
Binary file test-data/keras_batch_model02 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_batch_model03
Binary file test-data/keras_batch_model03 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_batch_params01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_batch_params01.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,94 @@
+ 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
+@ epsilon epsilon: None
+@ 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, 'activation': 're
+@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'units': 10, 'activation':
+@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'activation': 'so
+@ 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_data validation_data: None
+@ 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, 'activation': 'relu'}
+@ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'relu'
+* 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, 'units': 10, 'activation': 'linear', 'use_bias': True, 'kerne
+@ 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__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, 'activation': 'softmax'}
+@ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'softmax'
+* 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 0985b0dd6f1a test-data/keras_model01
Binary file test-data/keras_model01 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_model02
Binary file test-data/keras_model02 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_model04
Binary file test-data/keras_model04 has changed
diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_params04.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_params04.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,85 @@
+ 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
+@ epsilon epsilon: None
+@ 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, 'activation': 'li
+@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'units': 1, 'activation': '
+@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'activation': 'li
+@ 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_data validation_data: None
+@ 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, 'activation': 'linear'}
+@ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'linear'
+* 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, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel
+@ 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__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, 'activation': 'linear'}
+@ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'linear'
+* 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.
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diff -r 000000000000 -r 0985b0dd6f1a test-data/keras_save_weights01.h5
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diff -r 000000000000 -r 0985b0dd6f1a test-data/lda_model01
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diff -r 000000000000 -r 0985b0dd6f1a test-data/lda_model02
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diff -r 000000000000 -r 0985b0dd6f1a test-data/lda_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/lda_prediction_result01.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
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+0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 000000000000 -r 0985b0dd6f1a test-data/lda_prediction_result02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/lda_prediction_result02.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,5 @@
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diff -r 000000000000 -r 0985b0dd6f1a test-data/log_loss.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/log_loss.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+log_loss :
+3.7248735402728403
diff -r 000000000000 -r 0985b0dd6f1a test-data/matthews_corrcoef.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/matthews_corrcoef.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,2 @@
+matthews_corrcoef :
+1.0
diff -r 000000000000 -r 0985b0dd6f1a test-data/ml_vis01.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ml_vis01.html Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,14 @@
+
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/ml_vis02.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ml_vis02.html Fri Nov 01 17:26:59 2019 -0400
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+
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/ml_vis03.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ml_vis03.html Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,14 @@
+
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/ml_vis04.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ml_vis04.html Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,14 @@
+
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/ml_vis05.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ml_vis05.html Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,14 @@
+
\ No newline at end of file
diff -r 000000000000 -r 0985b0dd6f1a test-data/ml_vis05.png
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diff -r 000000000000 -r 0985b0dd6f1a test-data/model_fit01
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diff -r 000000000000 -r 0985b0dd6f1a test-data/model_fit02
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diff -r 000000000000 -r 0985b0dd6f1a test-data/model_fit02.h5
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diff -r 000000000000 -r 0985b0dd6f1a test-data/model_pred01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 0985b0dd6f1a test-data/model_pred02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/model_pred02.tabular Fri Nov 01 17:26:59 2019 -0400
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diff -r 000000000000 -r 0985b0dd6f1a test-data/moons.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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+1.71834935009773 -0.19568255060349 1
+0.53746170975916 -0.38659930637300 1
+0.03205157757166 0.99948621620069 0
+0.87131870412339 0.49071755200394 0
diff -r 000000000000 -r 0985b0dd6f1a test-data/mv_result02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result02.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,11 @@
+Predicted
+1.578912095858962
+-1.199072894940544
+-0.7173258906076226
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+-0.6841926371423699
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diff -r 000000000000 -r 0985b0dd6f1a test-data/mv_result03.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result03.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,6 @@
+train_sizes_abs mean_train_scores std_train_scores mean_test_scores std_test_scores
+17 0.9668700841937653 0.00277836829836518 0.7008862995946905 0.03857541198731935
+56 0.9730008602419361 0.006839342612121988 0.7963376762427242 0.004846330083938778
+95 0.9728783377589098 0.0037790183626530663 0.814592845745573 0.020457691766770824
+134 0.9739086338111185 0.001627343246847077 0.7985540571195479 0.03954641079310707
+174 0.9726218628287785 0.0032867750457225182 0.8152971572131146 0.04280261115004303
diff -r 000000000000 -r 0985b0dd6f1a test-data/mv_result05.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result05.tabular Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,262 @@
+Predicted
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diff -r 000000000000 -r 0985b0dd6f1a test-data/named_steps.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/named_steps.txt Fri Nov 01 17:26:59 2019 -0400
@@ -0,0 +1,6 @@
+{'preprocessing_1': SelectKBest(k=10, score_func=