# HG changeset patch
# User bgruening
# Date 1565349071 14400
# Node ID db511406350a912baa771b832e1525425f05fc6a

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty

diff -r 000000000000 -r db511406350a README.rst
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.rst	Fri Aug 09 07:11:11 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 db511406350a keras_deep_learning.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_deep_learning.py	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,359 @@
+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
+
+
+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']:
+            params[key] = _handle_shape(value)
+
+        elif key.endswith('_regularizer'):
+            params[key] = _handle_regularizer(value)
+
+        elif key.endswith('_constraint'):
+            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)
+        other_options = options.pop('layer_options', {})
+        options.update(other_options)
+
+        # parameters needs special care
+        options = _handle_layer_parameters(options)
+
+        # 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)
+        other_options = options.pop('layer_options', {})
+        options.update(other_options)
+
+        # parameters needs special care
+        options = _handle_layer_parameters(options)
+        # 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 db511406350a keras_macros.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_macros.xml	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,1228 @@
+<macros>
+  <token name="@KERAS_VERSION@">0.4.0</token>
+
+  <xml name="macro_stdio">
+    <stdio>
+        <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error"/>
+    </stdio>
+  </xml>
+
+  <xml name="keras_optimizer_common" token_lr="0.01">
+    <section name="optimizer_options" title="Optimizer Advanced Options" expanded="false">
+      <param argument="lr" type="float" value="@LR@" optional="true" label="Learning rate" help="float >= 0"/>
+      <yield/>
+      <!--param argument="clipnorm" type="float" value="" optional="true" label="clipnorm" help="float >= 0"/-->
+      <!--param argument="clipvalue" type="float" value="" optional="true" label="clipvalue" help="float >= 0"/-->
+    </section>
+  </xml>
+
+  <xml name="keras_optimizer_common_more" token_lr="0.001">
+    <expand macro="keras_optimizer_common" lr="@LR@">
+      <param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>
+      <param argument="decay" type="float" value="0" optional="true" label="decay" help="Learning rate decay over each update."/>
+      <yield/>
+    </expand>
+  </xml>
+
+  <xml name="keras_activations">
+    <param argument="activation" type="select" label="Activation function">
+      <option value="linear" selected="true">None / linear (default)</option>
+      <option value="softmax">softmax</option>
+      <option value="elu">elu</option>
+      <option value="selu">selu</option>
+      <option value="softplus">softplus</option>
+      <option value="softsign">softsign</option>
+      <option value="relu">relu</option>
+      <option value="tanh">tanh</option>
+      <option value="sigmoid">sigmoid</option>
+      <option value="hard_sigmoid">hard_sigmoid</option>
+      <option value="exponential">tanh</option>
+    </param>
+  </xml>
+
+  <xml name="keras_initializers" token_argument="kernel_initializer" token_default_kernel="false" token_default_bias="false" token_default_embeddings="false">
+    <param argument="@ARGUMENT@" type="select" label="@ARGUMENT@">
+      <option value="zeros" selected="@DEFAULT_BIAS@">zero / zeros / Zeros</option>
+      <option value="ones">one / ones / Ones</option>
+      <option value="constant">constant / Constant</option>
+      <option value="random_normal">normal / random_normal / RandomNormal</option>
+      <option value="random_uniform" selected="@DEFAULT_EMBEDDINGS@">uniform / random_uniform / RandomUniform</option>
+      <option value="truncated_normal">truncated_normal / TruncatedNormal</option>
+      <option value="orthogonal">orthogonal / Orthogonal</option>
+      <option value="identity">identity / Identity</option>
+      <option value="glorot_normal">glorot_normal</option>
+      <option value="glorot_uniform" selected="@DEFAULT_KERNEL@">glorot_uniform</option>
+      <option value="he_normal">he_normal</option>
+      <option value="he_uniform">he_uniform</option>
+      <option value="lecun_normal">lecun_normal</option>
+      <option value="lecun_uniform">lecun_uniform</option>
+    </param>
+  </xml>
+
+  <xml name="keras_regularizers" token_argument="kernel_regularizer">
+    <param argument="@ARGUMENT@" type="text" value="(0. , 0.)" optional="true" label="@ARGUMENT@"
+            help="(l1, l2). l1/l2: float; L1/l2 regularization factor. (0., 0.) is equivalent to `None`"/>
+  </xml>
+
+  <xml name="keras_constraints_options">
+    <section name="constraint_options" title="Constraint Advanced Options" expanded="false">
+      <yield/>
+      <param argument="axis" type="text" value="0" help="Integer or list of integers. axis along which to calculate weight norms">
+        <sanitizer>
+          <valid initial="default">
+            <add value="["/>
+            <add value="]"/>
+          </valid>
+        </sanitizer>
+      </param>
+    </section>
+  </xml>
+
+  <xml name="keras_constraints" token_argument="kernel_constraint">
+    <conditional name="@ARGUMENT@">
+      <param argument="constraint_type" type="select" label="@ARGUMENT@">
+        <option value="None" selected="true">None (default)</option>
+        <option value="MaxNorm">maxnorm / max_norm / MaxNorm</option>
+        <option value="NonNeg">nonneg / non_neg / NonNeg</option>
+        <option value="UnitNorm">unitnorm / unit_norm / UnitNorm</option>
+        <option value="MinMaxNorm">min_max_norm / MinMaxNorm</option>
+      </param>
+      <when value="None"/>
+      <when value="MaxNorm">
+        <expand macro="keras_constraints_options">
+          <param argument="max_value" type="float" value="2" help="the maximum norm for the incoming weights."/>
+        </expand>
+      </when>
+      <when value="NonNeg">
+      </when>
+      <when value="UnitNorm">
+        <expand macro="keras_constraints_options"/>
+      </when>
+      <when value="MinMaxNorm">
+        <expand macro="keras_constraints_options">
+          <param argument="min_value" type="float" value="0." help="the minimum norm for the incoming weights."/>
+          <param argument="max_value" type="float" value="1." help="the maximum norm for the incoming weights."/>
+          <param argument="max_value" type="float" value="1." help="rate for enforcing the constraint."/>
+        </expand>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="keras_layer_types_core">
+    <option value="Dense">Dense</option>
+    <option value="Activation">Activation</option>
+    <option value="Dropout">Dropout</option>
+    <option value="Flatten">Flatten</option>
+    <option value="Reshape">Reshape</option>
+    <option value="Permute">Permute</option>
+    <option value="RepeatVector">RepeatVector</option>
+    <!--option value="Lambda">Lambda - Not supported</option-->
+    <option value="ActivityRegularization">ActivityRegularization</option>
+    <option value="Masking">Masking</option>
+    <option value="SpatialDropout1D">SpatialDropout1D</option>
+    <option value="SpatialDropout2D">SpatialDropout2D</option>
+    <option value="SpatialDropout3D">SpatialDropout3D</option>
+  </xml>
+
+  <xml name="keras_layer_types_Convolutional">
+    <option value="Conv1D">Conv1D</option>
+    <option value="Conv2D">Conv2D</option>
+    <option value="SeparableConv1D">SeparableConv1D</option>
+    <option value="SeparableConv2D">SeparableConv2D</option>
+    <option value="DepthwiseConv2D">DepthwiseConv2D</option>
+    <option value="Conv2DTranspose">Conv2DTranspose</option>
+    <option value="Conv3D">Conv3D</option>
+    <option value="Conv3DTranspose">Conv3DTranspose</option>
+    <option value="Cropping1D">Cropping1D</option>
+    <option value="Cropping2D">Cropping2D</option>
+    <option value="Cropping3D">Cropping3D</option>
+    <option value="UpSampling1D">UpSampling1D</option>
+    <option value="UpSampling2D">UpSampling2D</option>
+    <option value="UpSampling3D">UpSampling3D</option>
+    <option value="ZeroPadding1D">ZeroPadding1D</option>
+    <option value="ZeroPadding2D">ZeroPadding2D</option>
+    <option value="ZeroPadding3D">ZeroPadding3D</option>
+  </xml>
+
+  <xml name="keras_layer_types_Pooling">
+    <option value="MaxPooling1D">MaxPooling1D</option>
+    <option value="MaxPooling2D">MaxPooling2D</option>
+    <option value="MaxPooling3D">MaxPooling3D</option>
+    <option value="AveragePooling1D">AveragePooling1D</option>
+    <option value="AveragePooling2D">AveragePooling2D</option>
+    <option value="AveragePooling3D">AveragePooling3D</option>
+    <option value="GlobalMaxPooling1D">GlobalMaxPooling1D</option>
+    <option value="GlobalAveragePooling1D">GlobalAveragePooling1D</option>
+    <option value="GlobalMaxPooling2D">GlobalMaxPooling2D</option>
+    <option value="GlobalAveragePooling2D">GlobalAveragePooling2D</option>
+    <option value="GlobalMaxPooling3D">GlobalMaxPooling3D</option>
+    <option value="GlobalAveragePooling3D">GlobalAveragePooling3D</option>
+  </xml>
+
+  <xml name="keras_layer_types_locally_connected">
+    <option value="LocallyConnected1D">LocallyConnected1D</option>
+    <option value="LocallyConnected2D">LocallyConnected2D</option>
+  </xml>
+
+  <xml name="keras_layer_types_recurrent">
+    <option value="RNN">RNN</option>
+    <option value="Masking">Masking</option>
+    <option value="SimpleRNN">SimpleRNN</option>
+    <option value="GRU">GRU</option>
+    <option value="LSTM">LSTM</option>
+    <option value="ConvLSTM2D">ConvLSTM2D</option>
+    <option value="ConvLSTM2DCell">ConvLSTM2DCell</option>
+    <option value="SimpleRNNCell">SimpleRNNCell</option>
+    <option value="GRUCell">GRUCell</option>
+    <option value="LSTMCell">LSTMCell</option>
+    <option value="CuDNNGRU">CuDNNGRU</option>
+    <option value="CuDNNLSTM">Dense</option>
+  </xml>
+
+  <xml name="keras_layer_types_embedding">
+    <option value="Embedding">Embedding</option>
+  </xml>
+
+  <xml name="keras_layer_types_advanced_activations">
+    <option value="LeakyReLU">LeakyReLU</option>
+    <option value="PReLU">PReLU</option>
+    <option value="ELU">ELU</option>
+    <option value="ThresholdedReLU">ThresholdedReLU</option>
+    <option value="Softmax">Softmax</option>
+    <option value="ReLU">ReLU</option>
+  </xml>
+
+  <xml name="keras_layer_types_normalization">
+    <option value="BatchNormalization">BatchNormalization</option>
+  </xml>
+
+  <xml name="keras_layer_types_noise">
+    <option value="GaussianNoise">GaussianNoise</option>
+    <option value="GaussianDropout">GaussianDropout</option>
+    <option value="AlphaDropout">AlphaDropout</option>    
+  </xml>
+
+  <xml name="keras_layer_types_merge">
+    <option value="Add">Add</option>
+    <option value="Subtract">Subtract</option>
+    <option value="Multiply">Multiply</option>
+    <option value="Average">Average</option>
+    <option value="Maximum">Maximum</option>
+    <option value="Minimum">Minimum</option>
+    <option value="Concatenate">Concatenate</option>
+    <option value="Dot">Dot</option>
+  </xml>
+
+  <!--Core Layers-->
+
+  <xml name="layer_Dense">
+    <param argument="units" type="integer" value="" optional="false" label="units" help="Positive integer, dimensionality of the output space."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true" />
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Dropout">
+    <param argument="rate" type="float" value="" min="0." max="1.0" help="Fraction of the input units to drop."/>
+    <param argument="noise_shape" type="text" value="" help="1D integer tensor representing the shape of the
+            binary dropout mask that will be multiplied with the input."/>
+    <param argument="seed" type="integer" value="" min="0" optional="true" help="A Python integer to use as random seed."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Flatten">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last">channels_last</option>
+      <option value="channels_first">channels_first</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Input">
+    <param argument="shape" type="text" value="" help="A shape tuple (integer), not including the batch size.For instance, `shape=(32,)`"/>
+    <!--param argument="batch_shape" type="text" value="" optional="true" help="A shape tuple (integer), including the batch size.For instance, `batch_shape=(10, 32)`"/-->
+    <param argument="name" type="text" value="" optional="true" help="An optional string name for the layer. Unique. autogenerated if it isn't provided."/>
+    <param argument="dtype" type="select" help="The data type expected by the input">
+      <option value="float32" selected="true">float32</option>
+      <option value="float64">float64</option>
+      <option value="int32">int32</option>
+      <option value="int64">int64</option>
+      <!--TODO add more DTYPEs-->
+    </param>
+    <param argument="sparse" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+    <!--param argument="tensor" type="data" format="zip" help="Optional existing tensor to wrap into the `Input` layer."/ -->
+    <yield/>
+  </xml>
+
+  <xml name="layer_Reshape">
+    <param argument="target_shape" type="text" value="" help="Tuple of integers. Does not include the batch axis."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Permute">
+    <param argument="dims" type="text" value="" help="Tuple of integers. Permutation pattern, does not include the samples dimension. 
+            Indexing starts at 1. For instance, (2, 1) permutes the first and second dimension of the input."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_RepeatVector">
+    <param argument="n" type="integer" value="" help="repetition factor"/>
+  </xml>
+
+  <xml name="layer_Lambda">
+    <param argument="function" type="text" value="lambda x: " help="The function to be evaluated. Only lambda function is supported!"/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ActivityRegularization">
+    <param argument="l1" type="float" value="0." min="0." help="L1 regularization factor (positive float)."/>
+    <param argument="l2" type="float" value="0." min="0." help="L2 regularization factor (positive float)."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Masking">
+    <param argument="mask_value" type="float" value="0." help="Masks a sequence by using a mask value to skip timesteps."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SpatialDropout1D">
+    <param argument="rate" type="float" value="" min="0." max="1." help="Fraction of the input units to drop."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SpatialDropout2D">
+    <param argument="rate" type="float" value="" min="0." max="1." help="Fraction of the input units to drop."/>
+    <param argument="data_format" type="select">
+      <option value="channels_last" selected="true">channels_last - the channels dimension (the depth) is at index 3</option>
+      <option value="channels_first">channels_first - the channels dimension (the depth) is at index 1</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SpatialDropout3D">
+    <param argument="rate" type="float" value="" min="0." max="1." help="Fraction of the input units to drop."/>
+    <param argument="data_format" type="select">
+      <option value="channels_last" selected="true">channels_last - the channels dimension (the depth) is at index 4</option>
+      <option value="channels_first">channels_first - the channels dimension (the depth) is at index 1</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Convolutional Layers-->
+
+  <xml name="layer_Conv1D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of a single integer, specifying the length of the 1D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of a single integer, specifying the stride length of the convolution."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <option value="causal">causal - causal (dilated) convolutions</option>
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SeparableConv1D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of a single integer, specifying the length of the 1D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of single integer, specifying the stride length of the convolution. "/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. "/>
+      <param argument="depth_multiplier" type="integer" value="1" help="The number of depthwise convolution output channels for each input channel."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" argument="depthwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="pointwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" argument="depthwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="pointwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="depthwise_constraint"/>
+      <expand macro="keras_constraints" argument="pointwise_constraint"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_SeparableConv2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <param argument="depth_multiplier" type="integer" value="1" help="The number of depthwise convolution output channels for each input channel."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" argument="depthwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="pointwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" argument="depthwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="pointwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="depthwise_constraint"/>
+      <expand macro="keras_constraints" argument="pointwise_constraint"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_DepthwiseConv2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <param argument="depth_multiplier" type="integer" value="1" help="The number of depthwise convolution output channels for each input channel."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" argument="depthwise_initializer" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" argument="depthwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="pointwise_regularizer"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="depthwise_constraint"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv2DTranspose">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="output_padding" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the amount of padding along the height and width of the output tensor."/>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" />
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv3D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 3 integers, specifying the strides of the convolution along each spatial dimension."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" />
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Conv3DTranspose">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of 3 integers, specifying the strides of the convolution along each spatial dimension."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="output_padding" type="text" value="" help="An integer or tuple/list of 3 integers, specifying the amount of padding along the depth, height, and width. "/>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+      </param>
+      <param argument="dilation_rate" type="text" value="1" help="an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution."/>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers" />
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Cropping1D">
+    <param argument="cropping" type="text" value="(1, 1)" help="int or tuple of int (length 2) How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1).
+              If a single int is provided, the same value will be used for both."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Cropping2D">
+    <param argument="cropping" type="text" value="((0, 0), (0, 0))" help="int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Cropping3D">
+    <param argument="cropping" type="text" value="((1, 1), (1, 1), (1, 1))" help="int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_UpSampling1D">
+    <param argument="size" type="integer" value="2" min="0" help="integer. Upsampling factor."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_UpSampling2D">
+    <param argument="size" type="text" value="(2, 2)" help="int, or tuple of 2 integers. The upsampling factors for rows and columns."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <param argument="interpolation" type="select">
+      <option value="nearest" selected="true">nearest</option>
+      <option value="bilinear">bilinear</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_UpSampling3D">
+    <param argument="size" type="text" value="(2, 2, 2)" help="int, or tuple of 3 integers. The upsampling factors for dim1, dim2 and dim3."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ZeroPadding1D">
+    <param argument="padding" type="text" value="1" help="int, or tuple of int (length 2)"/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ZeroPadding2D">
+    <param argument="padding" type="text" value="(1, 1)" help="int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ZeroPadding3D">
+    <param argument="padding" type="text" value="(1, 1, 1)" help="int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints."/>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Pooling Layers-->
+
+  <xml name="layer_MaxPooling1D">
+    <param name="pool_size" type="integer" value="2" help="Integer, size of the max pooling windows."/>
+    <param name="strides" type="integer" value="" help="Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_MaxPooling2D">
+    <param name="pool_size" type="text" value="(2, 2)" help="integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal)"/>
+    <param name="strides" type="text" value="" help="Integer, tuple of 2 integers, or None. Strides values. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_MaxPooling3D">
+    <param name="pool_size" type="text" value="(2, 2, 2)" help="tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). 
+                                                                (2, 2, 2) will halve the size of the 3D input in each dimension."/>
+    <param name="strides" type="text" value="" help="tuple of 3 integers, or None. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_AveragePooling1D">
+    <param name="pool_size" type="integer" value="2" help="Integer, size of the max pooling windows."/>
+    <param name="strides" type="integer" value="" help="Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_AveragePooling2D">
+    <param name="pool_size" type="text" value="(2, 2)" help="integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal)"/>
+    <param name="strides" type="text" value="" help=" Integer, tuple of 2 integers, or None. Strides values. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_AveragePooling3D">
+    <param name="pool_size" type="text" value="(2, 2, 2)" help="tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). 
+                                                                (2, 2, 2) will halve the size of the 3D input in each dimension."/>
+    <param name="strides" type="text" value="" help="tuple of 3 integers, or None. If None, it will default to pool_size."/>
+    <param argument="padding" type="select" >
+      <option value="valid" selected="true">valid - no padding</option>
+      <option value="same">same - output has the same length as the original input</option>
+      <!--option value="causal">causal - causal (dilated) convolutions</option-->
+    </param>
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalMaxPooling1D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalMaxPooling2D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalMaxPooling3D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalAveragePooling1D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalAveragePooling2D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_GlobalAveragePooling3D">
+    <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+      <option value="channels_last" selected="true">channels_last - inputs with shape (batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)</option>
+      <option value="channels_first">channels_first - inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Locally-connected Layers-->
+
+  <xml name="layer_LocallyConnected1D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of a single integer, specifying the length of the 1D convolution window."/>
+    <param argument="strides" type="text" value="1" help="An integer or tuple/list of a single integer, specifying the stride length of the convolution."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape  (batch, steps, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, steps)</option>
+      </param>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <xml name="layer_LocallyConnected2D">
+    <param argument="filters" type="integer" value="" min="0" help="the dimensionality of the output space (i.e. the number of output filters in the convolution)."/>
+    <param argument="kernel_size" type="text" value="" help="An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window."/>
+    <param argument="strides" type="text" value="(1, 1)" help="An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="padding" type="select" help="">
+        <option value="valid" selected="true">valid - no padding</option>
+        <option value="same">same - output has the same length as the original input</option>
+        <!--option value="causal">causal - causal (dilated) convolutions</option-->
+      </param>
+      <param argument="data_format" type="select" help="The ordering of the dimensions in the inputs.">
+        <option value="channels_last" selected="true">channels_last - inputs with shape (batch, height, width, channels)</option>
+        <option value="channels_first">channels_first - inputs with shape (batch, channels, height, width)</option>
+      </param>
+      <expand macro="keras_activations"/>
+      <param argument="use_bias" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true"/>
+      <expand macro="keras_initializers" default_kernel="true"/>\
+      <expand macro="keras_initializers" argument="bias_initializer" default_bias="true"/>
+      <expand macro="keras_regularizers"/>
+      <expand macro="keras_regularizers" argument="bias_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints"/>
+      <expand macro="keras_constraints" argument="bias_constraint"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <!--Recurrent Layers>
+
+  <xml name="layer_RNN">
+    <param argument="cell" >
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <param argument="return_sequences" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="return_state" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="go_backwards" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="stateful" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="unroll" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" />
+      <param argument="input_dim" >
+      <param argument="input_length" >
+    </section>
+    <yield/>
+  </xml-->
+
+  <xml name="layer_LSTM">
+    <param argument="units" type="integer" value="" min="1" help="Positive integer, dimensionality of the output space."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <expand macro="keras_activations"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <!--Embedding Layers-->
+
+  <xml name="layer_Embedding">
+    <param argument="input_dim" type="integer" value="" min="0" help="int > 0. Size of the vocabulary, i.e. maximum integer index + 1."/>
+    <param argument="output_dim" type="integer" value="" min="0" help="int >= 0. Dimension of the dense embedding."/>
+    <section name="layer_options" title="Layer Advanced Options" expanded="false">
+      <expand macro="keras_initializers" argument="embeddings_initializer" default_embeddings="true"/>
+      <expand macro="keras_regularizers" argument="embeddings_regularizer"/>
+      <expand macro="keras_regularizers" argument="activity_regularizer"/>
+      <expand macro="keras_constraints" argument="embeddings_constraint"/>
+      <param argument="mask_zero" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false"/>
+      <param argument="input_length" type="integer" value="" optional="true" min="0" help="Length of input sequences. Required if connecting Flatten then Dense layers upstream"/>
+    </section>
+    <yield/>
+  </xml>
+
+  <!--Merge Layers-->
+
+  <xml name="layer_merge">
+    <param name="merging_layers" type="text" value="[ ]" label="Type the layer index numbers in a list" help="List of numbers">
+      <sanitizer>
+        <valid initial="default">
+          <add value="["/>
+          <add value="]"/>
+        </valid>
+      </sanitizer>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Concatenate" token_type="integer" token_default_value="-1" token_help="Axis along which to concatenate.">
+    <expand macro="layer_merge">
+      <param argument="axis" type="@TYPE@" value="@DEFAULT_VALUE@" help="@HELP@"/>
+    </expand>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Dot">
+    <expand macro="layer_Concatenate" type="text" default_value="" help="Integer or tuple of integers, axis or axes along which to take the dot product.">
+      <param argument="normalize" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to L2-normalize samples along the dot product axis before taking the dot product?"/>
+    </expand>
+    <yield/>
+  </xml>
+
+  <!--Advanced Activations Layers-->
+
+  <xml name="layer_LeakyReLU">
+    <param argument="alpha" type="float" value="0.3" min="0." help="float >= 0. Negative slope coefficient."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_PReLU">
+    <expand macro="keras_initializers" argument="alpha_initializer" default_bias="true"/>
+    <expand macro="keras_regularizers" argument="alpha_regularizer"/>
+    <expand macro="keras_constraints" argument="alpha_constraint"/>
+    <param argument="shared_axes" type="text" value="" help="the axes along which to share learnable parameters for the activation function. E.g. [1, 2]">
+      <sanitizer>
+        <valid initial="default">
+          <add value="["/>
+          <add value="]"/>
+        </valid>
+      </sanitizer>
+    </param>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ELU">
+    <param argument="alpha" type="float" value="1.0" help="scale for the negative factor."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ThresholdedReLU">
+    <param argument="theta" type="float" value="1.0" help="float >= 0. Threshold location of activation."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_Softmax">
+    <param argument="axis" type="integer" value="-1" help="Integer, axis along which the softmax normalization is applied."/>
+    <yield/>
+  </xml>
+
+  <xml name="layer_ReLU">
+    <param argument="max_value" type="float" value="" min="0." help="float >= 0. Maximum activation value."/>
+    <param argument="negative_slope" type="float" value="0." min="0." help="float >= 0. Negative slope coefficient."/>
+    <param argument="max_value" type="float" value="0." help="float. Threshold value for thresholded activation."/>
+    <yield/>
+  </xml>
+
+  <!--Normalization Layers-->
+
+  <!--Noise layers-->
+
+  <xml name="inbound_nodes_index">
+    <param name="inbound_nodes" type="integer" value="" label="Type the index number of input layer"
+          help="Find the index number at the left top corner of layer configuration block"/>
+  </xml>
+
+
+  <!-- Keras CallBacks -->
+
+  <xml name="keras_callbacks">
+    <repeat name="callbacks" min="1" max="5" title="callback">
+      <conditional name="callback_selection">
+        <param name="callback_type" type="select" label="Choose a callback">
+          <option value="None" selected="true">None</option>
+          <option value="EarlyStopping">EarlyStopping -- Stop training when a monitored quantity has stopped improving</option>
+          <!--option value="KerasTensorBoard">TensorBoard</option-->
+          <!--option value="KerasLearningRateScheduler">LearningRateScheduler</option-->
+          <!--option value="RemoteMonitor">RemoteMonitor - Stream events to a server</option> -->
+          <!--option value="ModelCheckpoint">ModelCheckpoint - Save the model after every epoch</option>-->
+          <option value="TerminateOnNaN">TerminateOnNaN -- Terminates training when a NaN loss is encountered.</option>
+          <option value="ReduceLROnPlateau">ReduceLROnPlateau -- Reduce learning rate when a metric has stopped improving</option>
+          <option value="CSVLogger">CSVLogger -- Streams epoch results to a csv file</option>
+        </param>
+        <when value="None"/>
+        <when value="EarlyStopping">
+          <expand macro="keras_callback_common_options">
+            <param argument="min_delta" type="float" value="0" optional="true" help="Minimum change in the monitored quantity to qualify as an improvement."/>
+            <param argument="patience" type="integer" value="0" min="0" help="Number of epochs with no improvement after which training will be stopped."/>
+            <param argument="baseline" type="float" value="" optional="true" help="Baseline value for the monitored quantity to reach. Training will stop if the model doesn't show improvement over the baseline."/>
+            <param argument="restore_best_weights" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether to restore model weights from the epoch with the best value of the monitored quantity"/>
+          </expand>
+        </when>
+        <!--when value="TensorBoard">
+          <param argument="histogram_freq" />
+          <param argument="batch_size" />
+          <param argument="write_graph" />
+          <param argument="write_grads" />
+          <param argument="write_images" />
+          <param argument="embeddings_layer_names" />
+          <param argument="embeddings_metadata" />
+          <param argument="embeddings_data" />
+          <param argument="update_freq" />
+        </when-->
+        <!--when value="RemoteMonitor">
+          <param argument="root" type="text" value="http://localhost:9000" help="Root url of the target server."/>
+          <param argument="path" type="text" value="/publish/epoch/end/" help="Path relative to root to which the events will be sent. E.g., root + '/publish/epoch/end/'"/>
+          <param argument="field" type="text" value="data" optional="true" help="JSON field under which the data will be stored. The field is used only if the payload is sent within a form (i.e. send_as_json is set to False)."/>
+          <param argument="headers" type="text" value="" optional="true" help="Dictionary; optional custom HTTP headers.">
+            <sanitizer>
+              <valid initial="default">
+                <add value="{"/>
+                <add value="}"/>
+              </valid>
+            </sanitizer>
+          </param>
+          <param argument="send_as_json" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="Whether the request should be send as application/json."/>
+        </when>
+        <when value="ModelCheckpoint">
+          <expand macro="keras_callback_common_options">
+            <param argument="save_best_only" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" help="If True, the latest best model according to the quantity monitored will not be overwritten."/>
+            <param argument="save_weights_only" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="If True, then only the model's weights will be saved, else the full model is saved."/>
+            <param argument="period" type="integer" value="1" min="1" help="Interval (number of epochs) between checkpoints."/>
+          </expand>
+        </when>-->
+        <when value="TerminateOnNaN"/>
+        <when value="ReduceLROnPlateau">
+          <expand macro="keras_callback_common_options">
+            <param argument="factor" type="float" value="0.1" help="Factor by which the learning rate will be reduced. new_lr = lr * factor"/>
+            <param argument="patience" type="integer" value="10" help="Number of epochs with no improvement after which learning rate will be reduced."/>
+            <param argument="min_delta" type="float" value="0.0001" help="Threshold for measuring the new optimum, to only focus on significant changes."/>
+            <param argument="cooldown" type="integer" value="0" help="Number of epochs to wait before resuming normal operation after lr has been reduced."/>
+            <param argument="min_lr" type="float" value="0" help="Lower bound on the learning rate."/>
+          </expand>
+        </when>
+        <when value="CSVLogger"/>
+      </conditional>
+    </repeat>
+  </xml>
+
+  <xml name="keras_callback_common_options">
+    <param argument="monitor" type="select" help="Quantity to be monitored.">
+      <option value="val_loss" selected="true">val_loss</option>
+      <option value="loss">loss</option>
+    </param>
+    <param argument="mode" type="select">
+      <option value="auto" selected="true">auto -- the direction is automatically inferred from the name of the monitored quantity</option>
+      <option value="min">min -- training will stop when the quantity monitored has stopped decreasing</option>
+      <option value="max">max -- training will stop when the quantity monitored has stopped increasing</option>
+    </param>
+    <yield/>
+  </xml>
+
+  <!--Batch online data generators-->
+
+  <xml name="params_fasta_dna_batch_generator">
+    <param argument="seq_length" type="integer" value="1000" optional="true" help="Integer. Sequence length or number of bases."/>
+    <param argument="shuffle" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true" help="Whether to shuffle the data between epochs."/>
+    <param argument="seed" type="integer" value="" optional="true" help="Integer, random seed for data shuffling"/>
+  </xml>
+
+  <xml name="params_fasta_protein_batch_generator">
+    <expand macro="params_fasta_dna_batch_generator"/>
+  </xml>
+
+  <xml name="params_genomic_interval_batch_generator">
+    <expand macro="params_fasta_dna_batch_generator"/>
+    <param argument="blacklist_regions" type="select" help="Tabix-indexed list of regions from which we should not output sequences">
+      <option value="hg38" selected="true">hg38</option>
+      <option value="hg19">hg19</option>
+    </param>
+    <param argument="center_bin_to_predict" type="integer" value="200" optional="true" help="Query the tabix-indexed file for a region of length."/>
+    <param argument="feature_thresholds" type="float" value="0.5"  optional="true" help="Threshold values to determine target value."/>
+    <param argument="random_state" type="integer" value="" optional="true" help="Random seed number, to control the sample position in each invertal."/>
+  </xml>
+
+  <xml name="params_image_batch_generator">
+  </xml>
+
+
+  <!--composite params macro-->
+
+  <xml name="keras_compile_params_section">
+    <section name="compile_params" title="Compile Parameters" expanded="true">
+      <param name="loss" type="select" label="Select a loss function">
+        <option value="binary_crossentropy" selected="true">binary_crossentropy</option>
+        <option value="mean_squared_error">mse / MSE/ mean_squared_error</option>
+        <option value="mean_absolute_error">mae / MAE / mean_absolute_error</option>
+        <option value="mean_absolute_percentage_error">mape / MAPE / mean_absolute_percentage_error</option>
+        <option value="mean_squared_logarithmic_error">msle / MSLE / mean_squared_logarithmic_error</option>
+        <option value="squared_hinge">squared_hinge</option>
+        <option value="hinge">hinge</option>
+        <option value="categorical_hinge">categorical_hinge</option>
+        <option value="logcosh">logcosh</option>
+        <option value="categorical_crossentropy">categorical_crossentropy</option>
+        <option value="sparse_categorical_crossentropy">sparse_categorical_crossentropy</option>
+        <option value="kullback_leibler_divergence">kld / KLD / kullback_leibler_divergence</option>
+        <option value="poisson">poisson</option>
+        <option value="cosine_proximity">cosine / cosine_proximity</option>
+      </param>
+      <conditional name="optimizer_selection">
+        <param name="optimizer_type" type="select" label="Select an optimizer">
+          <option value="SGD" selected="true">SGD - Stochastic gradient descent optimizer </option>
+          <option value="RMSprop">RMSprop - RMSProp optimizer </option>
+          <option value="Adagrad">Adagrad - Adagrad optimizer </option>
+          <option value="Adadelta">Adadelta - Adadelta optimizer </option>
+          <option value="Adam">Adam - Adam optimizer </option>
+          <option value="Adamax">Adamax - A variant of Adam based on the infinity norm </option>
+          <option value="Nadam">Nadam - Nesterov Adam optimizer </option>
+        </param>
+        <when value="SGD">
+          <expand macro="keras_optimizer_common">
+            <param argument="momentum" type="float" value="0" optional="true" label="Momentum"
+                help="float >= 0. Parameter that accelerates SGD in the relevant direction and dampens oscillations."/>
+            <param argument="decay" type="float" value="0" label="Decay" optional="true" help="float &gt;= 0. Learning rate decay over each update."/>
+            <param argument="nesterov" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to apply Nesterov momentum"/>
+          </expand>
+        </when>
+        <when value="RMSprop">
+          <expand macro="keras_optimizer_common_more" lr="0.001">
+            <param argument="rho" type="float" value="0.9" optional="true" label="rho" help="float &gt;= 0."/>
+          </expand>
+        </when>
+        <when value="Adagrad">
+          <expand macro="keras_optimizer_common_more" lr="0.001"/>
+        </when>
+        <when value="Adadelta">
+          <expand macro="keras_optimizer_common_more" lr="1.0">
+            <param argument="rho" type="float" value="0.95" optional="true" label="rho" help="float &gt;= 0."/>
+          </expand>
+        </when>
+        <when value="Adam">
+          <expand macro="keras_optimizer_common_more" lr="0.001">
+            <param argument="beta_1" type="float" value="0.9" optional="true" label="beta_1" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="amsgrad" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="false" label="Whether to apply the AMSGrad variant?" 
+                help="Refer to paper `On the Convergence of Adam and Beyond`"/>
+          </expand>
+        </when>
+        <when value="Adamax">
+          <expand macro="keras_optimizer_common_more" lr="0.002">
+            <param argument="beta_1" type="float" value="0.9" optional="true" label="beta_1" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+          </expand>
+        </when>
+        <when value="Nadam">
+          <expand macro="keras_optimizer_common" lr="0.002">
+            <param argument="beta_1" type="float" value="0.9" optional="true" label="beta_1" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 &lt; beta &lt; 1. Generally close to 1."/>
+            <param argument="epsilon" type="float" value="" label="epsilon" optional="true" help="Fuzz factor. If `None`, defaults to `K.epsilon()`"/>
+            <param argument="schedule_decay" type="float" value="0.004" optional="true" label="schedule_decay" help="float, 0 &lt; beta &lt; 1."/>
+          </expand>
+        </when>
+      </conditional>
+      <param name="metrics" type="select" optional="true" multiple="true" label="Select metrics">
+        <option value="acc" selected="true">acc / accruracy</option>
+        <option value="binary_accuracy">binary_accuracy</option>
+        <option value="categorical_accuracy">categorical_accuracy</option>
+        <option value="sparse_categorical_accuracy">sparse_categorical_accuracy</option>
+        <option value="mse">mse / MSE / mean_squared_error</option>
+        <option value="mae">mae / MAE / mean_absolute_error</option>
+        <option value="mae">mape / MAPE / mean_absolute_percentage_error</option>
+        <option value="cosine_proximity">cosine_proximity</option>
+        <option value="cosine">cosine</option>
+        <option value="none">none</option>
+      </param>
+    </section>
+  </xml>
+
+  <xml name="keras_fit_params_section">
+    <section name="fit_params" title="Fit Parameters" expanded="true">
+      <param name="epochs" type="integer" value="1" min="1" label="epochs"/>
+      <param name="batch_size" type="integer" value="32" optional="true" label="batch_size" help="Integer or blank for 32"/>
+      <param name="steps_per_epoch" type="integer" value="" optional="true" label="steps_per_epoch" help="The number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. The default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined."/>
+      <param name="validation_steps" type="integer" value="" optional="true" label="validation_steps" help="Default None. Total number of steps (batches of samples) to validate before stopping." />
+      <!--`validation_freq` will be available in next keras version-->
+      <!--param name="validation_freq" type="integer" value="1" optional="true" label="validation_freq" help="Integer only at current moment. If an integer, specifies how many training epochs to run before a new validation run is performed."/-->
+      <expand macro="keras_callbacks"/>
+    </section>
+  </xml>
+
+ <!--Citation-->
+  <xml name="keras_citation">
+    <citation type="bibtex">
+      @misc{chollet2015keras,
+        title={Keras},
+        url={https://keras.io},
+        author={Chollet, Fran\c{c}ois and others},
+        year={2015},
+        howpublished={https://keras.io},
+      }
+    </citation>
+  </xml>
+
+  <xml name="tensorflow_citation">
+    <citation type="bibtex">
+      @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},
+      }
+    </citation>
+  </xml>
+
+</macros>
\ No newline at end of file
diff -r 000000000000 -r db511406350a main_macros.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/main_macros.xml	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,1978 @@
+<macros>
+  <token name="@VERSION@">1.0.7.10</token>
+
+  <token name="@ENSEMBLE_VERSION@">0.2.0</token>
+
+  <xml name="python_requirements">
+      <requirements>
+          <requirement type="package" version="3.6">python</requirement>
+          <requirement type="package" version="0.7.10">Galaxy-ML</requirement>
+          <yield/>
+      </requirements>
+  </xml>
+
+  <xml name="macro_stdio">
+    <stdio>
+        <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error"/>
+    </stdio>
+  </xml>
+
+
+  <!--Generic interface-->
+
+  <xml name="sl_Conditional" token_train="tabular" token_data="tabular" token_model="txt">
+    <conditional name="selected_tasks">
+        <param name="selected_task" type="select" label="Select a Classification Task">
+            <option value="train" selected="true">Train a model</option>
+            <option value="load">Load a model and predict</option>
+        </param>
+        <when value="load">
+            <param name="infile_model" type="data" format="@MODEL@" label="Models" help="Select a model file."/>
+            <param name="infile_data" type="data" format="@DATA@" label="Data (tabular)" help="Select the dataset you want to classify."/>
+            <param name="header" type="boolean" optional="True" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
+            <conditional name="prediction_options">
+                <param name="prediction_option" type="select" label="Select the type of prediction">
+                    <option value="predict">Predict class labels</option>
+                    <option value="advanced">Include advanced options</option>
+                </param>
+                <when value="predict">
+                </when>
+                <when value="advanced">
+                </when>
+            </conditional>
+        </when>
+        <when value="train">
+            <conditional name="selected_algorithms">
+                <yield />
+            </conditional>
+        </when>
+    </conditional>
+  </xml>
+
+  <xml name="advanced_section">
+    <section name="options" title="Advanced Options" expanded="False">
+      <yield />
+    </section>
+  </xml>
+
+
+  <!--Generalized Linear Models-->
+  <xml name="loss" token_help=" " token_select="false">
+    <param argument="loss" type="select" label="Loss function"  help="@HELP@">
+        <option value="squared_loss" selected="@SELECT@">squared loss</option>
+        <option value="huber">huber</option>
+        <option value="epsilon_insensitive">epsilon insensitive</option>
+        <option value="squared_epsilon_insensitive">squared epsilon insensitive</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="penalty" token_help=" ">
+    <param argument="penalty" type="select" label="Penalty (regularization term)"  help="@HELP@">
+        <option value="l2" selected="true">l2</option>
+        <option value="l1">l1</option>
+        <option value="elasticnet">elastic net</option>
+        <option value="none">none</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="l1_ratio" token_default_value="0.15" token_help=" ">
+    <param argument="l1_ratio" type="float" value="@DEFAULT_VALUE@" label="Elastic Net mixing parameter" help="@HELP@"/>
+  </xml>
+
+  <xml name="epsilon" token_default_value="0.1" token_help="Used if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. ">
+    <param argument="epsilon" type="float" value="@DEFAULT_VALUE@" label="Epsilon (epsilon-sensitive loss functions only)" help="@HELP@"/>
+  </xml>
+
+  <xml name="learning_rate_s" token_help=" " token_selected1="false" token_selected2="false">
+    <param argument="learning_rate" type="select" optional="true" label="Learning rate schedule"  help="@HELP@">
+        <option value="optimal" selected="@SELECTED1@">optimal</option>
+        <option value="constant">constant</option>
+        <option value="invscaling" selected="@SELECTED2@">inverse scaling</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="eta0" token_default_value="0.0" token_help="Used with ‘constant’ or ‘invscaling’ schedules. ">
+    <param argument="eta0" type="float" value="@DEFAULT_VALUE@" label="Initial learning rate" help="@HELP@"/>
+  </xml>
+
+  <xml name="power_t" token_default_value="0.5" token_help=" ">
+    <param argument="power_t" type="float" value="@DEFAULT_VALUE@" label="Exponent for inverse scaling learning rate" help="@HELP@"/>
+  </xml>
+
+  <xml name="normalize" token_checked="false" token_help=" ">
+    <param argument="normalize" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Normalize samples before training" help=" "/>
+  </xml>
+
+  <xml name="copy_X" token_checked="true" token_help=" ">
+    <param argument="copy_X" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Use a copy of samples" help="If false, samples would be overwritten. "/>
+  </xml>
+
+  <xml name="ridge_params">
+    <expand macro="normalize"/>
+    <expand macro="alpha" default_value="1.0"/>
+    <expand macro="fit_intercept"/>
+    <expand macro="max_iter" default_value=""/>
+    <expand macro="tol" default_value="0.001" help_text="Precision of the solution. "/>
+    <!--class_weight-->
+    <expand macro="copy_X"/>
+    <param argument="solver" type="select" value="" label="Solver to use in the computational routines" help=" ">
+        <option value="auto" selected="true">auto</option>
+        <option value="svd">svd</option>
+        <option value="cholesky">cholesky</option>
+        <option value="lsqr">lsqr</option>
+        <option value="sparse_cg">sparse_cg</option>
+        <option value="sag">sag</option>
+    </param>
+    <expand macro="random_state"/>
+  </xml>
+
+  <!--Ensemble methods-->
+  <xml name="n_estimators" token_default_value="10" token_help=" ">
+    <param argument="n_estimators" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of trees in the forest" help="@HELP@"/>
+  </xml>
+
+  <xml name="max_depth" token_default_value="" token_help=" ">
+    <param argument="max_depth" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Maximum depth of the tree" help="@HELP@"/>
+  </xml>
+
+  <xml name="min_samples_split" token_type="integer" token_default_value="2" token_help=" ">
+    <param argument="min_samples_split" type="@TYPE@" optional="true" value="@DEFAULT_VALUE@" label="Minimum number of samples required to split an internal node" help="@HELP@"/>
+  </xml>
+
+  <xml name="min_samples_leaf" token_type="integer" token_default_value="1" token_label="Minimum number of samples in newly created leaves" token_help=" ">
+    <param argument="min_samples_leaf" type="@TYPE@" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP@"/>
+  </xml>
+
+  <xml name="min_weight_fraction_leaf" token_default_value="0.0" token_help=" ">
+    <param argument="min_weight_fraction_leaf" type="float" optional="true" value="@DEFAULT_VALUE@" label="Minimum weighted fraction of the input samples required to be at a leaf node" help="@HELP@"/>
+  </xml>
+
+  <xml name="max_leaf_nodes" token_default_value="" token_help=" ">
+    <param argument="max_leaf_nodes" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Maximum number of leaf nodes in best-first method" help="@HELP@"/>
+  </xml>
+
+  <xml name="min_impurity_decrease" token_default_value="0" token_help=" ">
+    <param argument="min_impurity_decrease" type="float" value="@DEFAULT_VALUE@" optional="true" label="The threshold value of impurity for stopping node splitting" help="@HELP@"/>
+  </xml>
+
+  <xml name="bootstrap" token_checked="true" token_help=" ">
+    <param argument="bootstrap" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="@CHECKED@" label="Use bootstrap samples for building trees." help="@HELP@"/>
+  </xml>
+
+  <xml name="criterion" token_help=" ">
+    <param argument="criterion" type="select" label="Function to measure the quality of a split"  help=" ">
+        <option value="gini" selected="true">Gini impurity</option>
+        <option value="entropy">Information gain</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="criterion2" token_help="">
+    <param argument="criterion" type="select" label="Function to measure the quality of a split" >
+      <option value="mse">mse - mean squared error</option>
+      <option value="mae">mae - mean absolute error</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="oob_score" token_checked="false" token_help=" ">
+    <param argument="oob_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Use out-of-bag samples to estimate the generalization error" help="@HELP@"/>
+  </xml>
+
+  <xml name="max_features">
+    <conditional name="select_max_features">
+      <param argument="max_features" type="select" label="max_features">
+        <option value="auto" selected="true">auto - max_features=n_features</option>
+        <option value="sqrt">sqrt - max_features=sqrt(n_features)</option>
+        <option value="log2">log2 - max_features=log2(n_features)</option>
+        <option value="number_input">I want to type the number in or input None type</option>
+      </param>
+      <when value="auto">
+      </when>
+      <when value="sqrt">
+      </when>
+      <when value="log2">
+      </when>
+      <when value="number_input">
+        <param name="num_max_features" type="float" value="" optional="true" label="Input max_features number:" help="If int, consider the number of features at each split; If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split."/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="verbose" token_default_value="0" token_help="If 1 then it prints progress and performance once in a while. If greater than 1 then it prints progress and performance for every tree.">
+    <param argument="verbose" type="integer" value="@DEFAULT_VALUE@" optional="true" label="Enable verbose output" help="@HELP@"/>
+  </xml>
+
+  <xml name="learning_rate" token_default_value="1.0" token_help=" ">
+    <param argument="learning_rate" type="float" optional="true" value="@DEFAULT_VALUE@" label="Learning rate" help="@HELP@"/>
+  </xml>
+
+  <xml name="subsample" token_help=" ">
+    <param argument="subsample" type="float" value="1.0" optional="true" label="The fraction of samples to be used for fitting the individual base learners" help="@HELP@"/>
+  </xml>
+
+  <xml name="presort">
+    <param argument="presort" type="select" label="Whether to presort the data to speed up the finding of best splits in fitting" >
+      <option value="auto" selected="true">auto</option>
+      <option value="true">true</option>
+      <option value="false">false</option>
+    </param>
+  </xml>
+
+  <!--Parameters-->
+  <xml name="tol" token_default_value="0.0" token_help_text="Early stopping heuristics based on the relative center changes. Set to default (0.0) to disable this convergence detection.">
+        <param argument="tol" type="float" optional="true" value="@DEFAULT_VALUE@" label="Tolerance" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_clusters" token_default_value="8">
+    <param argument="n_clusters" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of clusters" help=" "/>
+  </xml>
+
+  <xml name="fit_intercept" token_checked="true">
+    <param argument="fit_intercept" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Estimate the intercept" help="If false, the data is assumed to be already centered."/>
+  </xml>
+
+  <xml name="n_iter" token_default_value="5" token_help_text="The number of passes over the training data (aka epochs). ">
+    <param argument="n_iter" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="shuffle" token_checked="true" token_help_text=" " token_label="Shuffle data after each iteration">
+    <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="random_state" token_default_value="" token_help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data. A fixed seed allows reproducible results. default=None.">
+    <param argument="random_state" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Random seed number" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="warm_start" token_checked="true" token_help_text="When set to True, reuse the solution of the previous call to fit as initialization,otherwise, just erase the previous solution.">
+    <param argument="warm_start" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Perform warm start" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="C" token_default_value="1.0" token_help_text="Penalty parameter C of the error term.">
+    <param argument="C" type="float" optional="true" value="@DEFAULT_VALUE@" label="Penalty parameter" help="@HELP_TEXT@"/>
+  </xml>
+
+  <!--xml name="class_weight" token_default_value="" token_help_text="">
+    <param argument="class_weight" type="" optional="true" value="@DEFAULT_VALUE@" label="" help="@HELP_TEXT@"/>
+  </xml-->
+
+  <xml name="alpha" token_default_value="0.0001" token_help_text="Constant that multiplies the regularization term if regularization is used. ">
+    <param argument="alpha" type="float" optional="true" value="@DEFAULT_VALUE@" label="Regularization coefficient" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_samples" token_default_value="100" token_help_text="The total number of points equally divided among clusters.">
+    <param argument="n_samples" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of samples" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_features" token_default_value="2" token_help_text="Number of different numerical properties produced for each sample.">
+    <param argument="n_features" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of features" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="noise" token_default_value="0.0" token_help_text="Floating point number. ">
+    <param argument="noise" type="float" optional="true" value="@DEFAULT_VALUE@" label="Standard deviation of the Gaussian noise added to the data" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="C" token_default_value="1.0" token_help_text="Penalty parameter C of the error term. ">
+      <param argument="C" type="float" optional="true" value="@DEFAULT_VALUE@" label="Penalty parameter" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="max_iter" token_default_value="300" token_label="Maximum number of iterations per single run" token_help_text=" ">
+      <param argument="max_iter" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_init" token_default_value="10" >
+      <param argument="n_init" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of runs with different centroid seeds" help=" "/>
+  </xml>
+
+  <xml name="init">
+      <param argument="init" type="select" label="Centroid initialization method"  help="''k-means++'' selects initial cluster centers that speed up convergence. ''random'' chooses k observations (rows) at random from data as initial centroids.">
+          <option value="k-means++">k-means++</option>
+          <option value="random">random</option>
+      </param>
+  </xml>
+
+  <xml name="gamma" token_default_value="1.0" token_label="Scaling parameter" token_help_text=" ">
+    <param argument="gamma" type="float" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="degree" token_default_value="3" token_label="Degree of the polynomial" token_help_text=" ">
+    <param argument="degree" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="coef0" token_default_value="1" token_label="Zero coefficient" token_help_text=" ">
+    <param argument="coef0" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="pos_label" token_default_value="">
+    <param argument="pos_label" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Label of the positive class" help=" "/>
+  </xml>
+
+  <xml name="average">
+    <param argument="average" type="select" optional="true" label="Averaging type" help=" ">
+      <option value="micro">Calculate metrics globally by counting the total true positives, false negatives and false positives. (micro)</option>
+      <option value="samples">Calculate metrics for each instance, and find their average. Only meaningful for multilabel. (samples)</option>
+      <option value="macro">Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. (macro)</option>
+      <option value="weighted">Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall. (weighted)</option>
+      <option value="None">None</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="beta">
+    <param argument="beta" type="float" value="1.0" label="The strength of recall versus precision in the F-score" help=" "/>
+  </xml>
+
+
+  <!--Data interface-->
+
+  <xml name="samples_tabular" token_multiple1="false" token_multiple2="false">
+    <param name="infile1" type="data" format="tabular" label="Training samples dataset:"/>
+    <param name="header1" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_1">
+      <expand macro="samples_column_selector_options" multiple="@MULTIPLE1@"/>
+    </conditional>
+    <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/>
+    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_2">
+      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="@MULTIPLE2@" infile="infile2"/>
+    </conditional>
+    <yield/>
+  </xml>
+
+  <xml name="samples_column_selector_options" token_column_option="selected_column_selector_option" token_col_name="col1" token_multiple="False" token_infile="infile1">
+    <param name="@COLUMN_OPTION@" type="select" label="Choose how to select data by column:">
+      <option value="by_index_number" selected="true">Select columns by column index number(s)</option>
+      <option value="all_but_by_index_number">All columns EXCLUDING some by column index number(s)</option>
+      <option value="by_header_name">Select columns by column header name(s)</option>
+      <option value="all_but_by_header_name">All columns EXCLUDING some by column header name(s)</option>
+      <option value="all_columns">All columns</option>
+    </param>
+    <when value="by_index_number">
+      <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/>
+    </when>
+    <when value="all_but_by_index_number">
+      <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" use_header_names="true" data_ref="@INFILE@" label="Select target column(s):"/>
+    </when>
+    <when value="by_header_name">
+      <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/>
+    </when>
+    <when value="all_but_by_header_name">
+      <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/>
+    </when>
+    <when value="all_columns">
+    </when>
+  </xml>
+
+  <xml name="clf_inputs_extended" token_label1=" " token_label2=" " token_multiple="False">
+    <conditional name="true_columns">
+      <param name="selected_input1" type="select" label="Select the input type of true labels dataset:">
+          <option value="tabular" selected="true">Tabular</option>
+          <option value="sparse">Sparse</option>
+      </param>
+      <when value="tabular">
+        <param name="infile1" type="data" label="@LABEL1@"/>
+        <param name="col1" type="data_column" data_ref="infile1" label="Select the target column:"/>
+      </when>
+      <when value="sparse">
+          <param name="infile1" type="data" format="txt" label="@LABEL1@"/>
+      </when>
+    </conditional>
+    <conditional name="predicted_columns">
+      <param name="selected_input2" type="select" label="Select the input type of predicted labels dataset:">
+          <option value="tabular" selected="true">Tabular</option>
+          <option value="sparse">Sparse</option>
+      </param>
+      <when value="tabular">
+        <param name="infile2" type="data" label="@LABEL2@"/>
+        <param name="col2" multiple="@MULTIPLE@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
+      </when>
+      <when value="sparse">
+          <param name="infile2" type="data" format="txt" label="@LABEL1@"/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="clf_inputs" token_label1="Dataset containing true labels (tabular):" token_label2="Dataset containing predicted values (tabular):" token_multiple1="False" token_multiple="False">
+    <param name="infile1" type="data" format="tabular" label="@LABEL1@"/>
+    <param name="header1" type="boolean" optional="True" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_1">
+      <expand macro="samples_column_selector_options" multiple="@MULTIPLE1@"/>
+    </conditional>
+    <param name="infile2" type="data" format="tabular" label="@LABEL2@"/>
+    <param name="header2" type="boolean" optional="True" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_2">
+      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="@MULTIPLE@" infile="infile2"/>
+    </conditional>
+  </xml>
+
+  <xml name="multiple_input" token_name="input_files" token_max_num="10" token_format="txt" token_label="Sparse matrix file (.mtx, .txt)" token_help_text="Specify a sparse matrix file in .txt format.">
+    <repeat name="@NAME@" min="1" max="@MAX_NUM@" title="Select input file(s):">
+        <param name="input" type="data" format="@FORMAT@" label="@LABEL@" help="@HELP_TEXT@"/>
+    </repeat>
+  </xml>
+
+  <xml name="sparse_target" token_label1="Select a sparse matrix:" token_label2="Select the tabular containing true labels:" token_multiple="False" token_format1="txt" token_format2="tabular" token_help1="" token_help2="">
+    <param name="infile1" type="data" format="@FORMAT1@" label="@LABEL1@" help="@HELP1@"/>
+    <expand macro="input_tabular_target"/>
+  </xml>
+
+  <xml name="sl_mixed_input">
+    <conditional name="input_options">
+      <param name="selected_input" type="select" label="Select input type:">
+          <option value="tabular" selected="true">tabular data</option>
+          <option value="sparse">sparse matrix</option>
+          <option value="seq_fasta">sequnences in a fasta file</option>
+          <option value="refseq_and_interval">reference genome and intervals</option>
+      </param>
+      <when value="tabular">
+          <expand macro="samples_tabular" multiple1="true" multiple2="false"/>
+      </when>
+      <when value="sparse">
+          <expand macro="sparse_target"/>
+      </when>
+      <when value="seq_fasta">
+          <expand macro="inputs_seq_fasta"/>
+      </when>
+      <when value="refseq_and_interval">
+          <expand macro="inputs_refseq_and_interval"/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="input_tabular_target">
+    <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/>
+    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_2">
+      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="false" infile="infile2"/>
+    </conditional>
+  </xml>
+
+  <xml name="inputs_seq_fasta">
+    <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays."/>
+    <expand macro="input_tabular_target"/>
+  </xml>
+
+  <xml name="inputs_refseq_and_interval">
+    <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence"/>
+    <param name="interval_file" type="data" format="interval" label="Dataset containing sequence intervals for training" help="interval. Sequences will be retrieved from the reference genome and one-hot encoded to training arrays."/>
+    <param name="target_file" type="data" format="bed" label="Dataset containing positions and features for target values." help="bed. The file will be compressed with `bgzip` and then indexed using `tabix`."/>
+    <param name="infile2" type="data" format="tabular" label="Dataset containing the feature list for prediction"/>
+    <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
+    <conditional name="column_selector_options_2">
+      <expand macro="samples_column_selector_options" column_option="selected_column_selector_option2" col_name="col2" multiple="true" infile="infile2"/>
+    </conditional>
+  </xml>
+
+  <!--Advanced options-->
+  <xml name="nn_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <yield/>
+      <param argument="weights" type="select" label="Weight function" help="Used in prediction.">
+          <option value="uniform" selected="true">Uniform weights. All points in each neighborhood are weighted equally. (Uniform)</option>
+          <option value="distance">Weight points by the inverse of their distance. (Distance)</option>
+      </param>
+      <param argument="algorithm" type="select" label="Neighbor selection algorithm" help=" ">
+          <option value="auto" selected="true">Auto</option>
+          <option value="ball_tree">BallTree</option>
+          <option value="kd_tree">KDTree</option>
+          <option value="brute">Brute-force</option>
+      </param>
+      <param argument="leaf_size" type="integer" value="30" label="Leaf size" help="Used with BallTree and KDTree. Affects the time and memory usage of the constructed tree."/>
+      <!--param name="metric"-->
+      <!--param name="p"-->
+      <!--param name="metric_params"-->
+    </section>
+  </xml>
+
+  <xml name="svc_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+        <yield/>
+        <param argument="kernel" type="select" optional="true" label="Kernel type" help="Kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used.">
+            <option value="rbf" selected="true">rbf</option>
+            <option value="linear">linear</option>
+            <option value="poly">poly</option>
+            <option value="sigmoid">sigmoid</option>
+            <option value="precomputed">precomputed</option>
+        </param>
+        <param argument="degree" type="integer" optional="true" value="3" label="Degree of the polynomial (polynomial kernel only)" help="Ignored by other kernels. dafault : 3 "/>
+        <!--TODO: param argument="gamma" float, optional (default=’auto’) -->
+        <param argument="coef0" type="float" optional="true" value="0.0" label="Zero coefficient (polynomial and sigmoid kernels only)"
+            help="Independent term in kernel function. dafault: 0.0 "/>
+        <param argument="shrinking" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use the shrinking heuristic" help=" "/>
+        <param argument="probability" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false"
+            label="Enable probability estimates. " help="This must be enabled prior to calling fit, and will slow down that method."/>
+        <!-- param argument="cache_size"-->
+        <!--expand macro="class_weight"/-->
+        <expand macro="tol" default_value="0.001" help_text="Tolerance for stopping criterion. "/>
+        <expand macro="max_iter" default_value="-1" label="Solver maximum number of iterations" help_text="Hard limit on iterations within solver, or -1 for no limit."/>
+        <!--param argument="decision_function_shape"-->
+        <expand macro="random_state" help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data for probability estimation. A fixed seed allows reproducible results."/>
+    </section>
+  </xml>
+
+  <xml name="spectral_clustering_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+        <expand macro="n_clusters"/>
+        <param argument="eigen_solver" type="select" value="" label="Eigen solver" help="The eigenvalue decomposition strategy to use.">
+            <option value="arpack" selected="true">arpack</option>
+            <option value="lobpcg">lobpcg</option>
+            <option value="amg">amg</option>
+            <!--None-->
+        </param>
+        <expand macro="random_state"/>
+        <expand macro="n_init"/>
+        <param argument="gamma" type="float" optional="true" value="1.0" label="Kernel scaling factor" help="Scaling factor of RBF, polynomial, exponential chi^2 and sigmoid affinity kernel. Ignored for affinity=''nearest_neighbors''."/>
+        <param argument="affinity" type="select" label="Affinity" help="Affinity kernel to use. ">
+            <option value="rbf" selected="true">RBF</option>
+            <option value="precomputed">precomputed</option>
+            <option value="nearest_neighbors">Nearset neighbors</option>
+        </param>
+        <param argument="n_neighbors" type="integer" optional="true" value="10" label="Number of neighbors" help="Number of neighbors to use when constructing the affinity matrix using the nearest neighbors method. Ignored for affinity=''rbf''"/>
+        <!--param argument="eigen_tol"-->
+        <param argument="assign_labels" type="select" label="Assign labels" help="The strategy to use to assign labels in the embedding space.">
+            <option value="kmeans" selected="true">kmeans</option>
+            <option value="discretize">discretize</option>
+        </param>
+        <param argument="degree" type="integer" optional="true" value="3"
+            label="Degree of the polynomial (polynomial kernel only)" help="Ignored by other kernels. dafault : 3 "/>
+        <param argument="coef0" type="integer" optional="true" value="1"
+            label="Zero coefficient (polynomial and sigmoid kernels only)" help="Ignored by other kernels. dafault : 1 "/>
+        <!--param argument="kernel_params"-->
+    </section>
+  </xml>
+
+  <xml name="minibatch_kmeans_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+        <expand macro="n_clusters"/>
+        <expand macro="init"/>
+        <expand macro="n_init" default_value="3"/>
+        <expand macro="max_iter" default_value="100"/>
+        <expand macro="tol" help_text="Early stopping heuristics based on normalized center change. To disable set to 0.0 ."/>
+        <expand macro="random_state"/>
+        <param argument="batch_size" type="integer" optional="true" value="100" label="Batch size" help="Size of the mini batches."/>
+        <!--param argument="compute_labels"-->
+        <param argument="max_no_improvement" type="integer" optional="true" value="10" label="Maximum number of improvement attempts" help="
+        Convergence detection based on inertia (the consecutive number of mini batches that doe not yield an improvement on the smoothed inertia).
+        To disable, set max_no_improvement to None. "/>
+        <param argument="init_size" type="integer" optional="true" value="" label="Number of random initialization samples" help="Number of samples to randomly sample for speeding up the initialization . ( default: 3 * batch_size )"/>
+        <param argument="reassignment_ratio" type="float" optional="true" value="0.01" label="Re-assignment ratio" help="Controls the fraction of the maximum number of counts for a center to be reassigned. Higher values yield better clustering results."/>
+    </section>
+  </xml>
+
+  <xml name="kmeans_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <expand macro="n_clusters"/>
+      <expand macro="init"/>
+      <expand macro="n_init"/>
+      <expand macro="max_iter"/>
+      <expand macro="tol" default_value="0.0001" help_text="Relative tolerance with regards to inertia to declare convergence."/>
+      <!--param argument="precompute_distances"/-->
+      <expand macro="random_state"/>
+      <param argument="copy_x" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Use a copy of data for precomputing distances" help="Mofifying the original data introduces small numerical differences caused by subtracting and then adding the data mean."/>
+      <expand macro="kmeans_algorithm"/>
+    </section>
+  </xml>
+
+  <xml name="kmeans_algorithm">
+    <param argument="algorithm" type="select" label="K-means algorithm to use:">
+      <option value="auto" selected="true">auto</option>
+      <option value="full">full</option>
+      <option value="elkan">elkan</option>
+    </param>
+  </xml>
+
+  <xml name="birch_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <param argument="threshold" type="float" optional="true" value="0.5" label="Subcluster radius threshold" help="The radius of the subcluster obtained by merging a new sample; the closest subcluster should be less than the threshold to avoid a new subcluster."/>
+      <param argument="branching_factor" type="integer" optional="true" value="50" label="Maximum number of subclusters per branch" help="Maximum number of CF subclusters in each node."/>
+      <expand macro="n_clusters" default_value="3"/>
+      <!--param argument="compute_labels"/-->
+    </section>
+  </xml>
+
+  <xml name="dbscan_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <param argument="eps" type="float" optional="true" value="0.5" label="Maximum neighborhood distance" help="The maximum distance between two samples for them to be considered as in the same neighborhood."/>
+      <param argument="min_samples" type="integer" optional="true" value="5" label="Minimal core point density" help="The number of samples (or total weight) in a neighborhood for a point (including the point itself) to be considered as a core point."/>
+      <param argument="metric" type="text" optional="true" value="euclidean" label="Metric" help="The metric to use when calculating distance between instances in a feature array."/>
+      <param argument="algorithm" type="select" label="Pointwise distance computation algorithm" help="The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors.">
+          <option value="auto" selected="true">auto</option>
+          <option value="ball_tree">ball_tree</option>
+          <option value="kd_tree">kd_tree</option>
+          <option value="brute">brute</option>
+      </param>
+      <param argument="leaf_size" type="integer" optional="true" value="30" label="Leaf size" help="Leaf size passed to BallTree or cKDTree. Memory and time efficieny factor in tree constrution and querying."/>
+    </section>
+  </xml>
+
+  <xml name="clustering_algorithms_options">
+    <conditional name="algorithm_options">
+      <param name="selected_algorithm" type="select" label="Clustering Algorithm">
+          <option value="KMeans" selected="true">KMeans</option>
+          <option value="SpectralClustering">Spectral Clustering</option>
+          <option value="MiniBatchKMeans">Mini Batch KMeans</option>
+          <option value="DBSCAN">DBSCAN</option>
+          <option value="Birch">Birch</option>
+      </param>
+      <when value="KMeans">
+          <expand macro="kmeans_advanced_options"/>
+      </when>
+      <when value="DBSCAN">
+          <expand macro="dbscan_advanced_options"/>
+      </when>
+      <when value="Birch">
+          <expand macro="birch_advanced_options"/>
+      </when>
+      <when value="SpectralClustering">
+          <expand macro="spectral_clustering_advanced_options"/>
+      </when>
+      <when value="MiniBatchKMeans">
+          <expand macro="minibatch_kmeans_advanced_options"/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="distance_metrics">
+    <param argument="metric" type="select" label="Distance metric" help=" ">
+      <option value="euclidean" selected="true">euclidean</option>
+      <option value="cityblock">cityblock</option>
+      <option value="cosine">cosine</option>
+      <option value="l1">l1</option>
+      <option value="l2">l2</option>
+      <option value="manhattan">manhattan</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="distance_nonsparse_metrics">
+    <option value="braycurtis">braycurtis</option>
+    <option value="canberra">canberra</option>
+    <option value="chebyshev">chebyshev</option>
+    <option value="correlation">correlation</option>
+    <option value="dice">dice</option>
+    <option value="hamming">hamming</option>
+    <option value="jaccard">jaccard</option>
+    <option value="kulsinski">kulsinski</option>
+    <option value="mahalanobis">mahalanobis</option>
+    <option value="matching">matching</option>
+    <option value="minkowski">minkowski</option>
+    <option value="rogerstanimoto">rogerstanimoto</option>
+    <option value="russellrao">russellrao</option>
+    <option value="seuclidean">seuclidean</option>
+    <option value="sokalmichener">sokalmichener</option>
+    <option value="sokalsneath">sokalsneath</option>
+    <option value="sqeuclidean">sqeuclidean</option>
+    <option value="yule">yule</option>
+  </xml>
+
+  <xml name="pairwise_kernel_metrics">
+    <param argument="metric" type="select" label="Pirwise Kernel metric" help=" ">
+      <option value="rbf" selected="true">rbf</option>
+      <option value="sigmoid">sigmoid</option>
+      <option value="polynomial">polynomial</option>
+      <option value="linear" selected="true">linear</option>
+      <option value="chi2">chi2</option>
+      <option value="additive_chi2">additive_chi2</option>
+    </param>
+  </xml>
+
+  <xml name="sparse_pairwise_metric_functions">
+    <param name="selected_metric_function" type="select" label="Select the pairwise metric you want to compute:">
+      <option value="euclidean_distances" selected="true">Euclidean distance matrix</option>
+      <option value="pairwise_distances">Distance matrix</option>
+      <option value="pairwise_distances_argmin">Minimum distances between one point and a set of points</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="pairwise_metric_functions">
+    <option value="additive_chi2_kernel" >Additive chi-squared kernel</option>
+    <option value="chi2_kernel">Exponential chi-squared kernel</option>
+    <option value="linear_kernel">Linear kernel</option>
+    <option value="manhattan_distances">L1 distances</option>
+    <option value="pairwise_kernels">Kernel</option>
+    <option value="polynomial_kernel">Polynomial kernel</option>
+    <option value="rbf_kernel">Gaussian (rbf) kernel</option>
+    <option value="laplacian_kernel">Laplacian kernel</option>
+  </xml>
+
+  <xml name="sparse_pairwise_condition">
+    <when value="pairwise_distances">
+      <section name="options" title="Advanced Options" expanded="False">
+          <expand macro="distance_metrics">
+              <yield/>
+          </expand>
+      </section>
+    </when>
+    <when value="euclidean_distances">
+      <section name="options" title="Advanced Options" expanded="False">
+          <param argument="squared" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false"
+            label="Return squared Euclidean distances" help=" "/>
+      </section>
+    </when>
+  </xml>
+
+  <xml name="argmin_distance_condition">
+    <when value="pairwise_distances_argmin">
+      <section name="options" title="Advanced Options" expanded="False">
+          <param argument="axis" type="integer" optional="true" value="1" label="Axis" help="Axis along which the argmin and distances are to be computed."/>
+          <expand macro="distance_metrics">
+              <yield/>
+          </expand>
+          <param argument="batch_size" type="integer" optional="true" value="500" label="Batch size" help="Number of rows to be processed in each batch run."/>
+      </section>
+    </when>
+  </xml>
+
+  <xml name="sparse_preprocessors">
+    <param name="selected_pre_processor" type="select" label="Select a preprocessor:">
+      <option value="StandardScaler" selected="true">Standard Scaler (Standardizes features by removing the mean and scaling to unit variance)</option>
+      <option value="Binarizer">Binarizer (Binarizes data)</option>
+      <option value="MaxAbsScaler">Max Abs Scaler (Scales features by their maximum absolute value)</option>
+      <option value="Normalizer">Normalizer (Normalizes samples individually to unit norm)</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="sparse_preprocessors_ext">
+    <expand macro="sparse_preprocessors">
+      <option value="KernelCenterer">Kernel Centerer (Centers a kernel matrix)</option>
+      <option value="MinMaxScaler">Minmax Scaler (Scales features to a range)</option>
+      <option value="PolynomialFeatures">Polynomial Features (Generates polynomial and interaction features)</option>
+      <option value="RobustScaler">Robust Scaler (Scales features using outlier-invariance statistics)</option>
+    </expand>
+  </xml>
+
+  <xml name="sparse_preprocessor_options">
+    <when value="Binarizer">
+        <section name="options" title="Advanced Options" expanded="False">
+            <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+                label="Use a copy of data for precomputing binarization" help=" "/>
+            <param argument="threshold" type="float" optional="true" value="0.0"
+                label="Threshold"
+                help="Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices. "/>
+        </section>
+    </when>
+    <when value="StandardScaler">
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use a copy of data for performing inplace scaling" help=" "/>
+        <param argument="with_mean" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Center the data before scaling" help=" "/>
+        <param argument="with_std" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Scale the data to unit variance (or unit standard deviation)" help=" "/>
+      </section>
+    </when>
+    <when value="MaxAbsScaler">
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use a copy of data for precomputing scaling" help=" "/>
+      </section>
+    </when>
+    <when value="Normalizer">
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="norm" type="select" optional="true" label="The norm to use to normalize non zero samples" help=" ">
+          <option value="l1" selected="true">l1</option>
+          <option value="l2">l2</option>
+          <option value="max">max</option>
+        </param>
+        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use a copy of data for precomputing row normalization" help=" "/>
+      </section>
+    </when>
+    <yield/>
+  </xml>
+
+  <xml name="sparse_preprocessor_options_ext">
+    <expand macro="sparse_preprocessor_options">
+      <when value="KernelCenterer">
+        <section name="options" title="Advanced Options" expanded="False">
+        </section>
+      </when>
+      <when value="MinMaxScaler">
+          <section name="options" title="Advanced Options" expanded="False">
+              <param argument="feature_range" type="text" value="(0, 1)" optional="true" help="Desired range of transformed data. None or tuple (min, max). None equals to (0, 1)"/>
+              <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
+                  label="Use a copy of data for precomputing normalization" help=" "/>
+          </section>
+      </when>
+      <when value="PolynomialFeatures">
+          <section name="options" title="Advanced Options" expanded="False">
+              <param argument="degree" type="integer" optional="true" value="2" label="The degree of the polynomial features " help=""/>
+              <param argument="interaction_only" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false" label="Produce interaction features only" help="(Features that are products of at most degree distinct input features) "/>
+              <param argument="include_bias" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Include a bias column" help="Feature in which all polynomial powers are zero "/>
+          </section>
+      </when>
+      <when value="RobustScaler">
+          <section name="options" title="Advanced Options" expanded="False">
+              <!--=True, =True, copy=True-->
+              <param argument="with_centering" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
+                  label="Center the data before scaling" help=" "/>
+              <param argument="with_scaling" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
+                  label="Scale the data to interquartile range" help=" "/>
+              <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
+                  label="Use a copy of data for inplace scaling" help=" "/>
+          </section>
+      </when>
+    </expand>
+  </xml>
+
+  <xml name="cv_splitter">
+    <option value="default" selected="true">default splitter</option>
+    <option value="KFold">KFold</option>
+    <option value="StratifiedKFold">StratifiedKFold</option>
+    <option value="LeaveOneOut">LeaveOneOut</option>
+    <option value="LeavePOut">LeavePOut</option>
+    <option value="RepeatedKFold">RepeatedKFold</option>
+    <option value="RepeatedStratifiedKFold">RepeatedStratifiedKFold</option>
+    <option value="ShuffleSplit">ShuffleSplit</option>
+    <option value="StratifiedShuffleSplit">StratifiedShuffleSplit</option>
+    <option value="TimeSeriesSplit">TimeSeriesSplit</option>
+    <option value="PredefinedSplit">PredefinedSplit</option>
+    <option value="OrderedKFold">OrderedKFold</option>
+    <option value="RepeatedOrderedKFold">RepeatedOrderedKFold</option>
+    <yield/>
+  </xml>
+
+  <xml name="cv_splitter_options">
+    <when value="default">
+      <expand macro="cv_n_splits"/>
+    </when>
+    <when value="KFold">
+      <expand macro="cv_n_splits"/>
+      <expand macro="cv_shuffle"/>
+      <expand macro="random_state"/>
+    </when>
+    <when value="StratifiedKFold">
+      <expand macro="cv_n_splits"/>
+      <expand macro="cv_shuffle"/>
+      <expand macro="random_state"/>
+    </when>
+    <when value="LeaveOneOut">
+    </when>
+    <when value="LeavePOut">
+      <param argument="p" type="integer" value="" label="p" help="Integer. Size of the test sets."/>
+    </when>
+    <when value="RepeatedKFold">
+      <expand macro="cv_n_splits" value="5"/>
+      <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." />
+      <expand macro="random_state" />
+    </when>
+    <when value="RepeatedStratifiedKFold">
+      <expand macro="cv_n_splits" value="5"/>
+      <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." />
+      <expand macro="random_state" />
+    </when>
+    <when value="ShuffleSplit">
+      <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/>
+      <expand macro="cv_test_size" value="0.1" />
+      <expand macro="random_state"/>
+    </when>
+    <when value="StratifiedShuffleSplit">
+      <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/>
+      <expand macro="cv_test_size" value="0.1" />
+      <expand macro="random_state"/>
+    </when>
+    <when value="TimeSeriesSplit">
+      <expand macro="cv_n_splits"/>
+      <param argument="max_train_size" type="integer" value="" optional="true" label="Maximum size of the training set" help="Maximum size for a single training set." />
+    </when>
+    <when value="PredefinedSplit">
+      <param argument="test_fold" type="text" value="" area="true" label="test_fold" help="List, e.g., [0, 1, -1, 1], represents two test sets, [X[0]] and [X[1], X[3]], X[2] is excluded from any test set due to '-1'."/>
+    </when>
+    <when value="OrderedKFold">
+      <expand macro="cv_n_splits"/>
+      <expand macro="cv_shuffle"/>
+      <expand macro="random_state"/>
+    </when>
+    <when value="RepeatedOrderedKFold">
+      <expand macro="cv_n_splits"/>
+      <param argument="n_repeats" type="integer" value="5"/>
+      <expand macro="random_state"/>
+    </when>
+    <yield/>
+  </xml>
+
+  <xml name="cv">
+    <conditional name="cv_selector">
+      <param name="selected_cv" type="select" label="Select the cv splitter:">
+        <expand macro="cv_splitter">
+          <option value="GroupKFold">GroupKFold</option>
+          <option value="GroupShuffleSplit">GroupShuffleSplit</option>
+          <option value="LeaveOneGroupOut">LeaveOneGroupOut</option>
+          <option value="LeavePGroupsOut">LeavePGroupsOut</option>
+        </expand>
+      </param>
+      <expand macro="cv_splitter_options">
+        <when value="GroupKFold">
+          <expand macro="cv_n_splits"/>
+          <expand macro="cv_groups" />
+        </when>
+        <when value="GroupShuffleSplit">
+          <expand macro="cv_n_splits" value="5"/>
+          <expand macro="cv_test_size"/>
+          <expand macro="random_state"/>
+          <expand macro="cv_groups"/>
+        </when>
+        <when value="LeaveOneGroupOut">
+          <expand macro="cv_groups"/>
+        </when>
+        <when value="LeavePGroupsOut">
+          <param argument="n_groups" type="integer" value="" label="n_groups" help="Number of groups (p) to leave out in the test split." />
+          <expand macro="cv_groups"/>
+        </when>
+      </expand>
+    </conditional>
+  </xml>
+
+  <xml name="cv_reduced" token_label="Select the cv splitter">
+    <conditional name="cv_selector">
+      <param name="selected_cv" type="select" label="@LABEL@">
+        <expand macro="cv_splitter"/>
+      </param>
+      <expand macro="cv_splitter_options"/>
+    </conditional>
+  </xml>
+
+  <xml name="cv_n_splits" token_value="3" token_help="Number of folds. Must be at least 2.">
+    <param argument="n_splits" type="integer" value="@VALUE@" min="1" label="n_splits" help="@HELP@"/>
+  </xml>
+
+  <xml name="cv_shuffle">
+    <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to shuffle data before splitting" />
+  </xml>
+
+  <xml name="cv_test_size" token_value="0.2">
+    <param argument="test_size" type="float" value="@VALUE@" min="0.0" label="Portion or number of the test set" help="0.0-1.0, proportion of the dataset to include in the test split; >1, integer only, the absolute number of test samples "/>
+  </xml>
+
+  <xml name="cv_groups" >
+    <section name="groups_selector" title="Groups column selector" expanded="true">
+      <param name="infile_g" type="data" format="tabular" label="Choose dataset containing groups info:"/>
+      <param name="header_g" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
+      <conditional name="column_selector_options_g">
+        <expand macro="samples_column_selector_options" column_option="selected_column_selector_option_g" col_name="col_g" multiple="False" infile="infile_g"/>
+      </conditional>
+    </section>
+  </xml>
+
+  <xml name="train_test_split_params">
+    <conditional name="split_algos">
+      <param name="shuffle" type="select" label="Select the splitting method">
+        <option value="None">No shuffle</option>
+        <option value="simple" selected="true">ShuffleSplit</option>
+        <option value="stratified">StratifiedShuffleSplit -- target values serve as class labels</option>
+        <option value="group">GroupShuffleSplit or split by group names</option>
+      </param>
+      <when value="None">
+        <expand macro="train_test_split_test_size"/>
+      </when>
+      <when value="simple">
+        <expand macro="train_test_split_test_size"/>
+        <expand macro="random_state"/>
+      </when>
+      <when value="stratified">
+        <expand macro="train_test_split_test_size"/>
+        <expand macro="random_state"/>
+      </when>
+      <when value="group">
+        <expand macro="train_test_split_test_size" optional="true"/>
+        <expand macro="random_state"/>
+        <param argument="group_names" type="text" value="" optional="true" label="Type in group names instead"
+        help="For example: chr6, chr7. This parameter is optional. If used, it will override the holdout size and random seed."/>
+        <yield/>
+      </when>
+    </conditional>
+    <!--param argument="train_size" type="float" optional="True" value="" label="Train size:"/>-->
+  </xml>
+
+  <xml name="train_test_split_test_size" token_optional="false">
+    <param name="test_size" type="float" value="0.2" optional="@OPTIONAL@" label="Holdout size" help="Leass than 1, for preportion; greater than 1 (integer), for number of samples."/>
+  </xml>
+
+  <xml name="feature_selection_algorithms">
+    <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option>
+    <option value="GenericUnivariateSelect">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option>
+    <option value="SelectPercentile">SelectPercentile - Select features according to a percentile of the highest scores</option>
+    <option value="SelectFpr">SelectFpr - Filter: Select the p-values below alpha based on a FPR test</option>
+    <option value="SelectFdr">SelectFdr - Filter: Select the p-values for an estimated false discovery rate</option>
+    <option value="SelectFwe">SelectFwe - Filter: Select the p-values corresponding to Family-wise error rate</option>
+    <option value="VarianceThreshold">VarianceThreshold - Feature selector that removes all low-variance features</option>
+    <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option>
+    <option value="RFE">RFE - Feature ranking with recursive feature elimination</option>
+    <option value="RFECV">RFECV - Feature ranking with recursive feature elimination and cross-validated selection of the best number of features</option>
+    <yield/>
+  </xml>
+
+  <xml name="feature_selection_algorithm_details">
+    <when value="GenericUnivariateSelect">
+      <expand macro="feature_selection_score_function" />
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="mode" type="select" label="Feature selection mode">
+          <option value="percentile">percentile</option>
+          <option value="k_best">k_best</option>
+          <option value="fpr">fpr</option>
+          <option value="fdr">fdr</option>
+          <option value="fwe">fwe</option>
+        </param>
+        <param argument="param" type="float" value="" optional="true" label="Parameter of the corresponding mode" help="float or int depending on the feature selection mode" />
+      </section>
+    </when>
+    <when value="SelectPercentile">
+      <expand macro="feature_selection_score_function" />
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="percentile" type="integer" value="10" optional="True" label="Percent of features to keep" />
+      </section>
+    </when>
+    <when value="SelectKBest">
+      <expand macro="feature_selection_score_function" />
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="k" type="integer" value="10" optional="True" label="Number of top features to select" help="No 'all' option is supported." />
+      </section>
+    </when>
+    <when value="SelectFpr">
+      <expand macro="feature_selection_score_function" />
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest p-value for features to be kept."/>
+      </section>
+    </when>
+    <when value="SelectFdr">
+      <expand macro="feature_selection_score_function" />
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/>
+      </section>
+    </when>
+    <when value="SelectFwe">
+      <expand macro="feature_selection_score_function" />
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/>
+      </section>
+    </when>
+    <when value="VarianceThreshold">
+      <section name="options" title="Options" expanded="False">
+        <param argument="threshold" type="float" value="0.0" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/>
+      </section>
+    </when>
+  </xml>
+
+  <xml name="feature_selection_SelectFromModel">
+    <when value="SelectFromModel">
+      <conditional name="model_inputter">
+        <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" >
+          <option value="new" selected="true">Yes</option>
+          <option value="prefitted">No. Load a prefitted estimator</option>
+        </param>
+        <when value="new">
+          <expand macro="estimator_selector_fs"/>
+        </when>
+        <when value="prefitted">
+          <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" />
+        </when>
+      </conditional>
+      <expand macro="feature_selection_SelectFromModel_options"/>
+    </when>
+  </xml>
+
+  <xml name="feature_selection_SelectFromModel_no_prefitted">
+    <when value="SelectFromModel">
+      <conditional name="model_inputter">
+        <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" >
+          <option value="new" selected="true">Yes</option>
+        </param>
+        <when value="new">
+          <expand macro="estimator_selector_all"/>
+        </when>
+      </conditional>
+      <expand macro="feature_selection_SelectFromModel_options"/>
+    </when>
+  </xml>
+
+  <xml name="feature_selection_SelectFromModel_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <param argument="threshold" type="text" value="" optional="true" label="threshold" help="The threshold value to use for feature selection. e.g. 'mean', 'median', '1.25*mean'." />
+      <param argument="norm_order" type="integer" value="1" label="norm_order" help="Order of the norm used to filter the vectors of coefficients below threshold in the case where the coef_ attribute of the estimator is of dimension 2. " />
+      <param argument="max_features" type="integer" value="" optional="true" label="The maximum number of features selected scoring above threshold" help="To disable threshold and only select based on max_features, set threshold=-np.inf."/>
+    </section>
+  </xml>
+
+  <xml name="feature_selection_RFE">
+    <when value="RFE">
+      <yield/>
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="n_features_to_select" type="integer" value="" optional="true" label="n_features_to_select" help="The number of features to select. If None, half of the features are selected." />
+        <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
+        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
+      </section>
+    </when>
+  </xml>
+
+  <xml name="feature_selection_RFECV_fs">
+    <when value="RFECV">
+      <yield/>
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
+        <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/>
+        <expand macro="cv"/>
+        <expand macro="scoring_selection"/>
+        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
+      </section>
+    </when>
+  </xml>
+
+  <xml name="feature_selection_RFECV_pipeline">
+    <when value="RFECV">
+      <yield/>
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " />
+        <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/>
+        <expand macro="cv_reduced"/>
+        <!-- TODO: group splitter support-->
+        <expand macro="scoring_selection"/>
+        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
+      </section>
+    </when>
+  </xml>
+
+  <xml name="feature_selection_DyRFECV_fs">
+    <when value="DyRFECV">
+      <yield/>
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="step" type="text" size="30" value="1" label="step" optional="true" help="Default = 1. Support float, int and list." >
+          <sanitizer>
+            <valid initial="default">
+              <add value="["/>
+              <add value="]"/>
+            </valid>
+          </sanitizer>
+        </param>
+        <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/>
+        <expand macro="cv"/>
+        <expand macro="scoring_selection"/>
+        <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." />
+      </section>
+    </when>
+  </xml>
+
+  <xml name="feature_selection_pipeline">
+    <!--compare to `feature_selection_fs`, no fitted estimator for SelectFromModel and no custom estimator for RFE and RFECV-->
+    <conditional name="fs_algorithm_selector">
+      <param name="selected_algorithm" type="select" label="Select a feature selection algorithm">
+        <expand macro="feature_selection_algorithms"/>
+      </param>
+      <expand macro="feature_selection_algorithm_details"/>
+      <expand macro="feature_selection_SelectFromModel_no_prefitted"/>
+      <expand macro="feature_selection_RFE">
+        <expand macro="estimator_selector_all"/>
+      </expand>  
+      <expand macro="feature_selection_RFECV_pipeline">
+        <expand macro="estimator_selector_all"/>
+      </expand>
+      <!-- TODO: add DyRFECV to pipeline-->
+    </conditional>
+  </xml>
+
+  <xml name="feature_selection_fs">
+    <conditional name="fs_algorithm_selector">
+      <param name="selected_algorithm" type="select" label="Select a feature selection algorithm">
+        <expand macro="feature_selection_algorithms">
+          <option value="DyRFECV">DyRFECV - Extended RFECV with changeable steps</option>
+        </expand>
+      </param>
+      <expand macro="feature_selection_algorithm_details"/>
+      <expand macro="feature_selection_SelectFromModel"/>
+      <expand macro="feature_selection_RFE">
+        <expand macro="estimator_selector_fs"/>
+      </expand>  
+      <expand macro="feature_selection_RFECV_fs">
+        <expand macro="estimator_selector_fs"/>
+      </expand>
+      <expand macro="feature_selection_DyRFECV_fs">
+        <expand macro="estimator_selector_fs"/>
+      </expand>
+    </conditional>
+  </xml>
+
+  <xml name="feature_selection_score_function">
+    <param argument="score_func" type="select" label="Select a score function">
+      <option value="chi2">chi2 - Compute chi-squared stats between each non-negative feature and class</option>
+      <option value="f_classif">f_classif - Compute the ANOVA F-value for the provided sample</option>
+      <option value="f_regression">f_regression - Univariate linear regression tests</option>
+      <option value="mutual_info_classif">mutual_info_classif - Estimate mutual information for a discrete target variable</option>
+      <option value="mutual_info_regression">mutual_info_regression - Estimate mutual information for a continuous target variable</option>
+    </param>
+  </xml>
+
+  <xml name="model_validation_common_options">
+    <expand macro="cv"/>
+    <expand macro="verbose"/>
+    <yield/>
+  </xml>
+
+  <xml name="scoring_selection">
+    <conditional name="scoring">
+      <param name="primary_scoring" type="select" multiple="false" label="Select the primary metric (scoring):" help="Metric to refit the best estimator.">
+        <option value="default" selected="true">default with estimator</option>
+        <option value="accuracy">Classification -- 'accuracy'</option>
+        <option value="balanced_accuracy">Classification -- 'balanced_accuracy'</option>
+        <option value="average_precision">Classification -- 'average_precision'</option>
+        <option value="f1">Classification -- 'f1'</option>
+        <option value="f1_micro">Classification -- 'f1_micro'</option>
+        <option value="f1_macro">Classification -- 'f1_macro'</option>
+        <option value="f1_weighted">Classification -- 'f1_weighted'</option>
+        <option value="f1_samples">Classification -- 'f1_samples'</option>
+        <option value="neg_log_loss">Classification -- 'neg_log_loss'</option>
+        <option value="precision">Classification -- 'precision'</option>
+        <option value="precision_micro">Classification -- 'precision_micro'</option>
+        <option value="precision_macro">Classification -- 'precision_macro'</option>
+        <option value="precision_wighted">Classification -- 'precision_wighted'</option>
+        <option value="precision_samples">Classification -- 'precision_samples'</option>
+        <option value="recall">Classification -- 'recall'</option>
+        <option value="recall_micro">Classification -- 'recall_micro'</option>
+        <option value="recall_macro">Classification -- 'recall_macro'</option>
+        <option value="recall_wighted">Classification -- 'recall_wighted'</option>
+        <option value="recall_samples">Classification -- 'recall_samples'</option>
+        <option value="roc_auc">Classification -- 'roc_auc'</option>
+        <option value="explained_variance">Regression -- 'explained_variance'</option>
+        <option value="neg_mean_absolute_error">Regression -- 'neg_mean_absolute_error'</option>
+        <option value="neg_mean_squared_error">Regression -- 'neg_mean_squared_error'</option>
+        <option value="neg_mean_squared_log_error">Regression -- 'neg_mean_squared_log_error'</option>
+        <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option>
+        <option value="r2">Regression -- 'r2'</option>
+        <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option>
+        <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option>
+      </param>
+      <when value="default"/>
+      <when value="accuracy"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="balanced_accuracy"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="average_precision"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="f1"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="f1_micro"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="f1_macro"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="f1_weighted"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="f1_samples"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="neg_log_loss"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="precision"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="precision_micro"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="precision_macro"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="precision_wighted"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="precision_samples"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="recall"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="recall_micro"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="recall_macro"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="recall_wighted"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="recall_samples"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="roc_auc"><expand macro="secondary_scoring_selection_classification"/></when>
+      <when value="explained_variance"><expand macro="secondary_scoring_selection_regression"/></when>
+      <when value="neg_mean_absolute_error"><expand macro="secondary_scoring_selection_regression"/></when>
+      <when value="neg_mean_squared_error"><expand macro="secondary_scoring_selection_regression"/></when>
+      <when value="neg_mean_squared_log_error"><expand macro="secondary_scoring_selection_regression"/></when>
+      <when value="neg_median_absolute_error"><expand macro="secondary_scoring_selection_regression"/></when>
+      <when value="r2"><expand macro="secondary_scoring_selection_regression"/></when>
+      <when value="binarize_auc_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when>
+      <when value="binarize_average_precision_scorer"><expand macro="secondary_scoring_selection_anormaly"/></when>
+    </conditional>
+  </xml>
+
+  <xml name="secondary_scoring_selection_classification">
+    <param name="secondary_scoring" type="select" multiple="true" label="Additional scoring used in multi-metric mode:" help="If the same metric with the primary is chosen, the metric will be ignored.">
+      <option value="accuracy">Classification -- 'accuracy'</option>
+      <option value="balanced_accuracy">Classification -- 'balanced_accuracy'</option>
+      <option value="average_precision">Classification -- 'average_precision'</option>
+      <option value="f1">Classification -- 'f1'</option>
+      <option value="f1_micro">Classification -- 'f1_micro'</option>
+      <option value="f1_macro">Classification -- 'f1_macro'</option>
+      <option value="f1_weighted">Classification -- 'f1_weighted'</option>
+      <option value="f1_samples">Classification -- 'f1_samples'</option>
+      <option value="neg_log_loss">Classification -- 'neg_log_loss'</option>
+      <option value="precision">Classification -- 'precision'</option>
+      <option value="precision_micro">Classification -- 'precision_micro'</option>
+      <option value="precision_macro">Classification -- 'precision_macro'</option>
+      <option value="precision_wighted">Classification -- 'precision_wighted'</option>
+      <option value="precision_samples">Classification -- 'precision_samples'</option>
+      <option value="recall">Classification -- 'recall'</option>
+      <option value="recall_micro">Classification -- 'recall_micro'</option>
+      <option value="recall_macro">Classification -- 'recall_macro'</option>
+      <option value="recall_wighted">Classification -- 'recall_wighted'</option>
+      <option value="recall_samples">Classification -- 'recall_samples'</option>
+      <option value="roc_auc">Classification -- 'roc_auc'</option>
+    </param>
+  </xml>
+
+  <xml name="secondary_scoring_selection_regression">
+    <param name="secondary_scoring" type="select" multiple="true" label="Additional scoring used in multi-metric mode:" help="If the same metric with the primary is chosen, the metric will be ignored.">
+      <option value="explained_variance">Regression -- 'explained_variance'</option>
+      <option value="neg_mean_absolute_error">Regression -- 'neg_mean_absolute_error'</option>
+      <option value="neg_mean_squared_error">Regression -- 'neg_mean_squared_error'</option>
+      <option value="neg_mean_squared_log_error">Regression -- 'neg_mean_squared_log_error'</option>
+      <option value="neg_median_absolute_error">Regression -- 'neg_median_absolute_error'</option>
+      <option value="r2">Regression -- 'r2'</option>
+    </param>
+  </xml>
+
+  <xml name="secondary_scoring_selection_anormaly">
+    <param name="secondary_scoring" type="select" multiple="true" label="Additional scoring used in multi-metric mode:" help="If the same metric with the primary is chosen, the metric will be ignored.">
+      <option value="binarize_auc_scorer">anomaly detection -- binarize_auc_scorer</option>
+      <option value="binarize_average_precision_scorer">anomaly detection -- binarize_average_precision_scorer</option>
+    </param>
+  </xml>
+
+  <xml name="pre_dispatch" token_type="hidden" token_default_value="all" token_help="Number of predispatched jobs for parallel execution">
+    <param argument="pre_dispatch" type="@TYPE@" value="@DEFAULT_VALUE@" optional="true" label="pre_dispatch" help="@HELP@"/>
+  </xml>
+
+  <xml name="search_cv_estimator">
+    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
+    <section name="search_params_builder" title="Search parameters Builder" expanded="true">
+      <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing parameter names" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/>
+      <repeat name="param_set" min="1" max="30" title="Parameter settings for search:">
+          <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)">
+            <options from_dataset="infile_params" startswith="@">
+              <column name="name" index="2"/>
+              <column name="value" index="1"/>
+              <filter type="unique_value" name="unique_param" column="1"/>
+            </options>
+          </param>
+          <param name="sp_list" type="text" value="" optional="true" label="Search list" help="list or array-like, for example: [1, 10, 100, 1000], [True, False] and ['auto', 'sqrt', None]. See `help` section for more examples">
+            <sanitizer>
+              <valid initial="default">
+                <add value="&apos;"/>
+                <add value="&quot;"/>
+                <add value="["/>
+                <add value="]"/>
+              </valid>
+            </sanitizer>
+          </param>
+      </repeat>
+    </section>
+  </xml>
+
+  <xml name="estimator_and_hyperparameter">
+    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
+    <section name="hyperparams_swapping" title="Hyperparameter Swapping" expanded="false">
+      <param name="infile_params" type="data" format="tabular" optional="true" label="Choose the dataset containing hyperparameters for the pipeline/estimator above" help="This dataset could be the output of `get_params` in the `Estimator Attributes` tool."/>
+      <repeat name="param_set" min="1" max="30" title="New hyperparameter setting">
+          <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)">
+            <options from_dataset="infile_params" startswith="@">
+              <column name="name" index="2"/>
+              <column name="value" index="1"/>
+              <filter type="unique_value" name="unique_param" column="1"/>
+            </options>
+          </param>
+          <param name="sp_value" type="text" value="" optional="true" label="New value" help="Supports int, float, boolean, single quoted string, and selected object constructor. Similar to the `Parameter settings for search` section in `searchcv` tool except that only single value is expected here.">
+            <sanitizer>
+              <valid initial="default">
+                <add value="&apos;"/>
+                <add value="&quot;"/>
+              </valid>
+            </sanitizer>
+          </param>
+      </repeat>
+    </section>
+  </xml>
+
+  <xml name="search_cv_options">
+      <expand macro="scoring_selection"/>
+      <expand macro="model_validation_common_options"/>
+      <!--expand macro="pre_dispatch" default_value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/-->
+      <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="If True, data is identically distributed across the folds"/>
+      <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset."/>
+      <param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/>
+      <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/>
+  </xml>
+
+  <xml name="estimator_module_options">
+      <option value="svm" selected="true">sklearn.svm</option>
+      <option value="linear_model">sklearn.linear_model</option>
+      <option value="ensemble">sklearn.ensemble</option>
+      <option value="naive_bayes">sklearn.naive_bayes</option>
+      <option value="tree">sklearn.tree</option>
+      <option value="neighbors">sklearn.neighbors</option>
+      <option value="xgboost">xgboost</option>
+      <yield/>
+  </xml>
+
+  <xml name="estimator_suboptions">
+      <when value="svm">
+        <param name="selected_estimator" type="select" label="Choose estimator class:">
+          <option value="LinearSVC" selected="true">LinearSVC</option>
+          <option value="LinearSVR">LinearSVR</option>
+          <option value="NuSVC">NuSVC</option>
+          <option value="NuSVR">NuSVR</option>
+          <option value="OneClassSVM">OneClassSVM</option>
+          <option value="SVC">SVC</option>
+          <option value="SVR">SVR</option>
+        </param>
+        <expand macro="estimator_params_text"/>
+      </when>
+      <when value="linear_model">
+        <param name="selected_estimator" type="select" label="Choose estimator class:">
+          <option value="ARDRegression" selected="true">ARDRegression</option>
+          <option value="BayesianRidge">BayesianRidge</option>
+          <option value="ElasticNet">ElasticNet</option>
+          <option value="ElasticNetCV">ElasticNetCV</option>
+          <option value="HuberRegressor">HuberRegressor</option>
+          <option value="Lars">Lars</option>
+          <option value="LarsCV">LarsCV</option>
+          <option value="Lasso">Lasso</option>
+          <option value="LassoCV">LassoCV</option>
+          <option value="LassoLars">LassoLars</option>
+          <option value="LassoLarsCV">LassoLarsCV</option>
+          <option value="LassoLarsIC">LassoLarsIC</option>
+          <option value="LinearRegression">LinearRegression</option>
+          <option value="LogisticRegression">LogisticRegression</option>
+          <option value="LogisticRegressionCV">LogisticRegressionCV</option>
+          <option value="MultiTaskLasso">MultiTaskLasso</option>
+          <option value="MultiTaskElasticNet">MultiTaskElasticNet</option>
+          <option value="MultiTaskLassoCV">MultiTaskLassoCV</option>
+          <option value="MultiTaskElasticNetCV">MultiTaskElasticNetCV</option>
+          <option value="OrthogonalMatchingPursuit">OrthogonalMatchingPursuit</option>
+          <option value="OrthogonalMatchingPursuitCV">OrthogonalMatchingPursuitCV</option>
+          <option value="PassiveAggressiveClassifier">PassiveAggressiveClassifier</option>
+          <option value="PassiveAggressiveRegressor">PassiveAggressiveRegressor</option>
+          <option value="Perceptron">Perceptron</option>
+          <option value="RANSACRegressor">RANSACRegressor</option>
+          <option value="Ridge">Ridge</option>
+          <option value="RidgeClassifier">RidgeClassifier</option>
+          <option value="RidgeClassifierCV">RidgeClassifierCV</option>
+          <option value="RidgeCV">RidgeCV</option>
+          <option value="SGDClassifier">SGDClassifier</option>
+          <option value="SGDRegressor">SGDRegressor</option>
+          <option value="TheilSenRegressor">TheilSenRegressor</option>
+        </param>
+        <expand macro="estimator_params_text"/>
+      </when>
+      <when value="ensemble">
+        <param name="selected_estimator" type="select" label="Choose estimator class:">
+          <option value="AdaBoostClassifier" selected="true">AdaBoostClassifier</option>
+          <option value="AdaBoostRegressor">AdaBoostRegressor</option>
+          <option value="BaggingClassifier">BaggingClassifier</option>
+          <option value="BaggingRegressor">BaggingRegressor</option>
+          <option value="ExtraTreesClassifier">ExtraTreesClassifier</option>
+          <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
+          <option value="GradientBoostingClassifier">GradientBoostingClassifier</option>
+          <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
+          <option value="IsolationForest">IsolationForest</option>
+          <option value="RandomForestClassifier">RandomForestClassifier</option>
+          <option value="RandomForestRegressor">RandomForestRegressor</option>
+          <option value="RandomTreesEmbedding">RandomTreesEmbedding</option>
+          <!--option value="VotingClassifier">VotingClassifier</option-->
+        </param>
+        <expand macro="estimator_params_text"/>
+      </when>
+      <when value="naive_bayes">
+        <param name="selected_estimator" type="select" label="Choose estimator class:">
+          <option value="BernoulliNB" selected="true">BernoulliNB</option>
+          <option value="GaussianNB">GaussianNB</option>
+          <option value="MultinomialNB">MultinomialNB</option>
+        </param>
+        <expand macro="estimator_params_text"/>
+      </when>
+      <when value="tree">
+        <param name="selected_estimator" type="select" label="Choose estimator class:">
+          <option value="DecisionTreeClassifier" selected="true">DecisionTreeClassifier</option>
+          <option value="DecisionTreeRegressor">DecisionTreeRegressor</option>
+          <option value="ExtraTreeClassifier">ExtraTreeClassifier</option>
+          <option value="ExtraTreeRegressor">ExtraTreeRegressor</option>
+        </param>
+        <expand macro="estimator_params_text"/>
+      </when>
+      <when value="neighbors">
+        <param name="selected_estimator" type="select" label="Choose estimator class:">
+          <option value="KNeighborsClassifier" selected="true">KNeighborsClassifier</option>
+          <option value="KNeighborsRegressor">KNeighborsRegressor</option>
+          <!--option value="BallTree">BallTree</option-->
+          <!--option value="KDTree">KDTree</option-->
+          <option value="KernelDensity">KernelDensity</option>
+          <option value="LocalOutlierFactor">LocalOutlierFactor</option>
+          <option value="RadiusNeighborsClassifier">RadiusNeighborsClassifier</option>
+          <option value="RadiusNeighborsRegressor">RadiusNeighborsRegressor</option>
+          <option value="NearestCentroid">NearestCentroid</option>
+          <option value="NearestNeighbors">NearestNeighbors</option>
+        </param>
+        <expand macro="estimator_params_text"/>
+      </when>
+      <when value="xgboost">
+        <param name="selected_estimator" type="select" label="Choose estimator class:">
+          <option value="XGBRegressor" selected="true">XGBRegressor</option>
+          <option value="XGBClassifier">XGBClassifier</option>
+        </param>
+        <expand macro="estimator_params_text"/>
+      </when>
+      <yield/>
+  </xml>
+
+  <xml name="estimator_selector_all">
+    <conditional name="estimator_selector">
+      <param name="selected_module" type="select" label="Choose the module that contains target estimator:" >
+        <expand macro="estimator_module_options"/>
+      </param>
+      <expand macro="estimator_suboptions"/>
+    </conditional>
+  </xml>
+
+  <xml name="estimator_selector_fs">
+    <conditional name="estimator_selector">
+      <param name="selected_module" type="select" label="Choose the module that contains target estimator:" >
+        <expand macro="estimator_module_options">
+            <option value="custom_estimator">Load a custom estimator</option>
+        </expand>
+      </param>
+      <expand macro="estimator_suboptions">
+        <when value="custom_estimator">
+            <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline:"/>
+        </when>
+      </expand>
+    </conditional>
+  </xml>
+
+  <xml name="estimator_params_text" token_label="Type in parameter settings if different from default:" token_default_value=''
+        token_help="Dictionary-capable, e.g., C=1, kernel='linear'. No double quotes. Leave this box blank for default estimator.">
+    <param name="text_params" type="text" value="@DEFAULT_VALUE@" optional="true" label="@LABEL@" help="@HELP@">
+      <sanitizer>
+        <valid initial="default">
+          <add value="&apos;"/>
+        </valid>
+      </sanitizer>
+    </param>
+  </xml>
+
+  <xml name="kernel_approximation_all">
+    <conditional name="kernel_approximation_selector">
+      <param name="select_algorithm" type="select" label="Choose a kernel approximation algorithm:">
+        <option value="Nystroem" selected="true">Nystroem</option>
+        <option value="RBFSampler">RBFSampler</option>
+        <option value="AdditiveChi2Sampler">AdditiveChi2Sampler</option>
+        <option value="SkewedChi2Sampler">SkewedChi2Sampler</option>
+      </param>
+      <when value="Nystroem">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): coef0=None, degree=None, gamma=None, kernel='rbf', kernel_params=None, n_components=100, random_state=None. No double quotes"/>
+      </when>
+      <when value="RBFSampler">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): gamma=1.0, n_components=100, random_state=None."/>
+      </when>
+      <when value="AdditiveChi2Sampler">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sample_interval=None, sample_steps=2."/>
+      </when>
+      <when value="SkewedChi2Sampler">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): n_components=100, random_state=None, skewedness=1.0."/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="matrix_decomposition_all">
+    <conditional name="matrix_decomposition_selector">
+      <param name="select_algorithm" type="select" label="Choose a matrix decomposition algorithm:">
+        <option value="DictionaryLearning" selected="true">DictionaryLearning</option>
+        <option value="FactorAnalysis">FactorAnalysis</option>
+        <option value="FastICA">FastICA</option>
+        <option value="IncrementalPCA">IncrementalPCA</option>
+        <option value="KernelPCA">KernelPCA</option>
+        <option value="LatentDirichletAllocation">LatentDirichletAllocation</option>
+        <option value="MiniBatchDictionaryLearning">MiniBatchDictionaryLearning</option>
+        <option value="MiniBatchSparsePCA">MiniBatchSparsePCA</option>
+        <option value="NMF">NMF</option>
+        <option value="PCA">PCA</option>
+        <option value="SparsePCA">SparsePCA</option>
+        <!--option value="SparseCoder">SparseCoder</option-->
+        <option value="TruncatedSVD">TruncatedSVD</option>
+      </param>
+      <when value="DictionaryLearning">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): alpha=1, code_init=None, dict_init=None, fit_algorithm='lars', max_iter=1000, n_components=None, random_state=None, split_sign=False, tol=1e-08, transform_algorithm='omp', transform_alpha=None, transform_n_nonzero_coefs=None, verbose=False."/>
+      </when>
+      <when value="FactorAnalysis">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): copy=True, iterated_power=3, max_iter=1000, n_components=None, noise_variance_init=None, random_state=0, svd_method='randomized', tol=0.01."/>
+      </when>
+      <when value="FastICA">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): algorithm='parallel', fun='logcosh', fun_args=None, max_iter=200, n_components=None, random_state=None, tol=0.0001, w_init=None, whiten=True. No double quotes."/>
+      </when>
+      <when value="IncrementalPCA">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): batch_size=None, copy=True, n_components=None, whiten=False."/>
+      </when>
+      <when value="KernelPCA">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto', fit_inverse_transform=False, gamma=None, kernel='linear', kernel_params=None, max_iter=None, n_components=None, random_state=None, remove_zero_eig=False, tol=0. No double quotes."/>
+      </when>
+      <when value="LatentDirichletAllocation">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): batch_size=128, doc_topic_prior=None, evaluate_every=-1, learning_decay=0.7, learning_method=None, learning_offset=10.0, max_doc_update_iter=100, max_iter=10, mean_change_tol=0.001, n_components=10, n_topics=None, perp_tol=0.1, random_state=None, topic_word_prior=None, total_samples=1000000.0, verbose=0."/>
+      </when>
+      <when value="MiniBatchDictionaryLearning">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): alpha=1, batch_size=3, dict_init=None, fit_algorithm='lars', n_components=None, n_iter=1000, random_state=None, shuffle=True, split_sign=False, transform_algorithm='omp', transform_alpha=None, transform_n_nonzero_coefs=None, verbose=False."/>
+      </when>
+      <when value="MiniBatchSparsePCA">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): alpha=1, batch_size=3, callback=None, method='lars', n_components=None, n_iter=100, random_state=None, ridge_alpha=0.01, shuffle=True, verbose=False."/>
+      </when>
+      <when value="NMF">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200, n_components=None, random_state=None, shuffle=False, solver='cd', tol=0.0001, verbose=0."/>
+      </when>
+      <when value="PCA">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): copy=True, iterated_power='auto', n_components=None, random_state=None, svd_solver='auto', tol=0.0, whiten=False."/>
+      </when>
+      <when value="SparsePCA">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): U_init=None, V_init=None, alpha=1, max_iter=1000, method='lars', n_components=None, random_state=None, ridge_alpha=0.01, tol=1e-08, verbose=False."/>
+      </when>
+      <when value="TruncatedSVD">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): algorithm='randomized', n_components=2, n_iter=5, random_state=None, tol=0.0."/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="FeatureAgglomeration">
+    <conditional name="FeatureAgglomeration_selector">
+      <param name="select_algorithm" type="select" label="Choose the algorithm:">
+        <option value="FeatureAgglomeration" selected="true">FeatureAgglomeration</option>
+      </param>
+      <when value="FeatureAgglomeration">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): affinity='euclidean', compute_full_tree='auto', connectivity=None, linkage='ward', memory=None, n_clusters=2, pooling_func=np.mean."/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="skrebate">
+    <conditional name="skrebate_selector">
+      <param name="select_algorithm" type="select" label="Choose the algorithm:">
+        <option value="ReliefF">ReliefF</option>
+        <option value="SURF">SURF</option>
+        <option value="SURFstar">SURFstar</option>
+        <option value="MultiSURF">MultiSURF</option>
+        <option value="MultiSURFstar">MultiSURFstar</option>
+        <!--option value="TuRF">TuRF</option> -->
+      </param>
+      <when value="ReliefF">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, n_neighbors=100, verbose=False."/>
+      </when>
+      <when value="SURF">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
+      </when>
+      <when value="SURFstar">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
+      </when>
+      <when value="MultiSURF">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
+      </when>
+      <when value="MultiSURFstar">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/>
+      </when>
+      <!--when value="TuRF">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): core_algorithm='ReliefF', discrete_threshold=10, n_features_to_select=10, n_neighbors=100, pct=0.5, verbose=False."/>
+      </when> -->
+    </conditional>
+  </xml>
+
+  <xml name="imbalanced_learn_sampling">
+    <conditional name="imblearn_selector">
+      <param name="select_algorithm" type="select" label="Choose the algorithm:">
+        <option value="under_sampling.ClusterCentroids" selected="true">under_sampling.ClusterCentroids</option>
+        <option value="under_sampling.CondensedNearestNeighbour">under_sampling.CondensedNearestNeighbour</option>
+        <option value="under_sampling.EditedNearestNeighbours">under_sampling.EditedNearestNeighbours</option>
+        <option value="under_sampling.RepeatedEditedNearestNeighbours">under_sampling.RepeatedEditedNearestNeighbours</option>
+        <option value="under_sampling.AllKNN">under_sampling.AllKNN</option>
+        <option value="under_sampling.InstanceHardnessThreshold">under_sampling.InstanceHardnessThreshold</option>
+        <option value="under_sampling.NearMiss">under_sampling.NearMiss</option>
+        <option value="under_sampling.NeighbourhoodCleaningRule">under_sampling.NeighbourhoodCleaningRule</option>
+        <option value="under_sampling.OneSidedSelection">under_sampling.OneSidedSelection</option>
+        <option value="under_sampling.RandomUnderSampler">under_sampling.RandomUnderSampler</option>
+        <option value="under_sampling.TomekLinks">under_sampling.TomekLinks</option>
+        <option value="over_sampling.ADASYN">over_sampling.ADASYN</option>
+        <option value="over_sampling.RandomOverSampler">over_sampling.RandomOverSampler</option>
+        <option value="over_sampling.SMOTE">over_sampling.SMOTE</option>
+        <option value="over_sampling.SVMSMOTE">over_sampling.SVMSMOTE</option>
+        <option value="over_sampling.BorderlineSMOTE">over_sampling.BorderlineSMOTE</option>
+        <option value="over_sampling.SMOTENC">over_sampling.SMOTENC</option>
+        <option value="combine.SMOTEENN">combine.SMOTEENN</option>
+        <option value="combine.SMOTETomek">combine.SMOTETomek</option>
+        <option value="Z_RandomOverSampler">Z_RandomOverSampler - for regression</option>
+      </param>
+      <when value="under_sampling.ClusterCentroids">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, estimator=None, voting='auto'."/>
+      </when>
+      <when value="under_sampling.CondensedNearestNeighbour">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/>
+      </when>
+      <when value="under_sampling.EditedNearestNeighbours">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/>
+      </when>
+      <when value="under_sampling.RepeatedEditedNearestNeighbours">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/>
+      </when>
+      <when value="under_sampling.AllKNN">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', allow_minority=False."/>
+      </when>
+      <when value="under_sampling.InstanceHardnessThreshold">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): estimator=None, sampling_strategy='auto', random_state=None, cv=5."/>
+      </when>
+      <when value="under_sampling.NearMiss">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3."/>
+      </when>
+      <when value="under_sampling.NeighbourhoodCleaningRule">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', threshold_cleaning=0.5."/>
+      </when>
+      <when value="under_sampling.OneSidedSelection">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/>
+      </when>
+      <when value="under_sampling.RandomUnderSampler">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, replacement=False."/>
+      </when>
+      <when value="under_sampling.TomekLinks">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None."/>
+      </when>
+      <when value="over_sampling.ADASYN">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=5."/>
+      </when>
+      <when value="over_sampling.RandomOverSampler">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None."/>
+      </when>
+      <when value="over_sampling.SMOTE">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, k_neighbors=5."/>
+      </when>
+      <when value="over_sampling.SVMSMOTE">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', k_neighbors=5, m_neighbors=10, out_step=0.5, random_state=None, svm_estimator=None."/>
+      </when>
+      <when value="over_sampling.BorderlineSMOTE">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', k_neighbors=5, kind='borderline-1', m_neighbors=10, random_state=None."/>
+      </when>
+      <when value="over_sampling.SMOTENC">
+        <expand macro="estimator_params_text"
+              help="Default: categorical_features=[], sampling_strategy='auto', random_state=None, k_neighbors=5."/>
+      </when>
+      <when value="combine.SMOTEENN">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, enn=None."/>
+      </when>
+      <when value="combine.SMOTETomek">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, tomek=None."/>
+      </when>
+      <when value="Z_RandomOverSampler">
+        <expand macro="estimator_params_text"
+              help="Default(=blank): sampling_strategy='auto', random_state=None, negative_thres=0, positive_thres=-1."/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="stacking_ensemble_inputs">
+    <section name="options" title="Advanced Options" expanded="false">
+        <yield/>
+        <param argument="use_features_in_secondary" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
+        <param argument="store_train_meta_features" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false"/>
+    </section>
+  </xml>
+
+  <xml name="stacking_base_estimator">
+    <conditional name="estimator_selector">
+        <param name="selected_module" type="select" label="Choose the module that contains target estimator:" >
+            <expand macro="estimator_module_options">
+                <option value="custom_estimator">Load a custom estimator</option>
+            </expand>
+        </param>
+        <expand macro="estimator_suboptions">
+            <when value="custom_estimator">
+                <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the custom estimator or pipeline"/>
+            </when>
+        </expand>
+    </conditional>
+  </xml>
+
+  <xml name="stacking_voting_weights">
+    <section name="options" title="Advanced Options" expanded="false">
+        <param argument="weights" type="text" value="[]" optional="true" help="Sequence of weights (float or int). Uses uniform weights if None (`[]`).">
+          <sanitizer>
+            <valid initial="default">
+              <add value="["/>
+              <add value="]"/>
+            </valid>
+          </sanitizer>
+        </param>
+        <yield/>
+    </section>
+  </xml>
+
+  <xml name="preprocessors_sequence_encoders">
+    <conditional name="encoder_selection">
+        <param name="encoder_type" type="select" label="Choose the sequence encoder class">
+            <option value="GenomeOneHotEncoder">GenomeOneHotEncoder</option>
+            <option value="ProteinOneHotEncoder">ProteinOneHotEncoder</option>
+        </param>
+        <when value="GenomeOneHotEncoder">
+            <expand macro="preprocessors_sequence_encoder_arguments"/>
+        </when>
+        <when value="ProteinOneHotEncoder">
+            <expand macro="preprocessors_sequence_encoder_arguments"/>
+        </when>
+    </conditional>
+  </xml>
+
+  <xml name="preprocessors_sequence_encoder_arguments">
+    <param argument="seq_length" type="integer" value="" min="0" optional="true" help="Integer. Sequence length"/>
+    <param argument="padding" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" help="Whether to pad or truncate sequence to meet the sequence length."/>
+  </xml>
+
+  <!-- Outputs -->
+
+  <xml name="output">
+    <outputs>
+      <data format="tabular" name="outfile_predict">
+          <filter>selected_tasks['selected_task'] == 'load'</filter>
+      </data>
+      <data format="zip" name="outfile_fit" label="${tool.name}.${selected_tasks.selected_algorithms.selected_algorithm}">
+          <filter>selected_tasks['selected_task'] == 'train'</filter>
+      </data>
+    </outputs>
+  </xml>
+
+  <!--Citations-->
+  <xml name="eden_citation">
+    <citations>
+        <citation type="doi">10.5281/zenodo.15094</citation>
+    </citations>
+  </xml>
+
+  <xml name="sklearn_citation">
+    <citations>
+        <citation type="bibtex">
+          @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}
+          }
+        </citation>
+        <yield/>
+    </citations>
+  </xml>
+
+  <xml name="scipy_citation">
+    <citations>
+        <citation type="bibtex">
+          @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]}
+        }
+        </citation>
+    </citations>
+  </xml>
+
+  <xml name="skrebate_citation">
+    <citation type="bibtex">
+      @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}
+      }
+    </citation>
+  </xml>
+
+  <xml name="xgboost_citation">
+    <citation type="bibtex">
+      @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},
+      }
+    </citation>
+  </xml>
+
+  <xml name="imblearn_citation">
+    <citation type="bibtex">
+      @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}
+      }
+    </citation>
+  </xml>
+
+  <xml name="selene_citation">
+    <citation type="bibtex">
+      @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}
+      }
+    </citation>
+  </xml>
+
+</macros>
diff -r 000000000000 -r db511406350a model_prediction.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/model_prediction.py	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,205 @@
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import warnings
+
+from scipy.io import mmread
+from sklearn.pipeline import Pipeline
+
+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()
+
+        preds = estimator.model_.predict_generator(
+            pred_data_generator.flow(batch_size=32),
+            workers=N_JOBS,
+            use_multiprocessing=True)
+
+        if preds.min() < 0. or preds.max() > 1.:
+            warnings.warn('Network returning invalid probability values. '
+                          'The last layer might not normalize predictions '
+                          'into probabilities '
+                          '(like softmax or sigmoid would).')
+
+        if params['method'] == 'predict_proba' and preds.shape[1] == 1:
+            # first column is probability of class 0 and second is of class 1
+            preds = np.hstack([1 - preds, preds])
+
+        elif params['method'] == 'predict':
+            if preds.shape[-1] > 1:
+                # if the last activation is `softmax`, the sum of all
+                # probibilities will 1, the classification is considered as
+                # multi-class problem, otherwise, we take it as multi-label.
+                act = getattr(estimator.model_.layers[-1], 'activation', None)
+                if act and act.__name__ == 'softmax':
+                    classes = preds.argmax(axis=-1)
+                else:
+                    preds = (preds > 0.5).astype('int32')
+            else:
+                classes = (preds > 0.5).astype('int32')
+
+            preds = estimator.classes_[classes]
+    # end input
+
+    # output
+    if input_type == 'variant_effect':   # TODO: save in batchs
+        rval = pd.DataFrame(preds)
+        meta = pd.DataFrame(
+            pred_data_generator.variants,
+            columns=['chrom', 'pos', 'name', 'ref', 'alt', 'strand'])
+
+        rval = pd.concat([meta, rval], axis=1)
+
+    elif 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 db511406350a model_prediction.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/model_prediction.xml	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,128 @@
+<tool id="model_prediction" name="Model Prediction" version="@VERSION@">
+    <description>predicts on new data using a preffited model</description>
+    <macros>
+        <import>main_macros.xml</import>
+        <import>keras_macros.xml</import>
+    </macros>
+    <expand macro="python_requirements"/>
+    <expand macro="macro_stdio"/>
+    <version_command>echo "@VERSION@"</version_command>
+    <command>
+        <![CDATA[
+        python '$__tool_directory__/model_prediction.py'
+            --inputs '$inputs'
+            --infile_estimator '$infile_estimator'
+            --outfile_predict '$outfile_predict'
+            --infile_weights '$infile_weights'
+            #if $input_options.selected_input == 'seq_fasta'
+            --fasta_path '$input_options.fasta_path'
+            #elif $input_options.selected_input == 'variant_effect'
+            --ref_seq '$input_options.ref_genome_file'
+            --vcf_path '$input_options.vcf_file'
+            #else
+            --infile1 '$input_options.infile1'
+            #end if
+        ]]>
+    </command>
+    <configfiles>
+        <inputs name="inputs" />
+    </configfiles>
+    <inputs>
+        <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>
+        <param name="infile_weights" type="data" format="h5" optional="true" label="Choose the dataset containing weights for the estimator above" help="Optional. For deep learning only."/>
+        <param argument="method" type="select" label="Select invocation method">
+            <option value="predict" selected="true">predict</option>
+            <option value="predict_proba">predict_proba</option>
+        </param>
+        <conditional name="input_options">
+            <param name="selected_input" type="select" label="Select input data type for prediction">
+                <option value="tabular" selected="true">tabular data</option>
+                <option value="sparse">sparse matrix</option>
+                <option value="seq_fasta">sequnences in a fasta file</option>
+                <option value="variant_effect">reference genome and variant call file</option>
+            </param>
+            <when value="tabular">
+                <param name="infile1" type="data" format="tabular" label="Training samples dataset:"/>
+                <param name="header1" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
+                <conditional name="column_selector_options_1">
+                    <expand macro="samples_column_selector_options" multiple="true"/>
+                </conditional>
+            </when>
+            <when value="sparse">
+                <param name="infile1" type="data" format="txt" label="Select a sparse matrix" help=""/>
+            </when>
+            <when value="seq_fasta">
+                <param name="fasta_path" type="data" format="fasta" label="Dataset containing fasta genomic/protein sequences" help="Sequences will be one-hot encoded to arrays."/>
+                <param name="seq_type" type="select" label="Sequence type">
+                    <option value="FastaDNABatchGenerator">DNA</option>
+                    <option value="FastaRNABatchGenerator">RNA</option>
+                    <option value="FastaProteinBatchGenerator">Protein</option>
+                </param>
+            </when>
+            <when value="variant_effect">
+                <param name="ref_genome_file" type="data" format="fasta" label="Dataset containing reference genomic sequence" help="fasta"/>
+                <param name="blacklist_regions" type="select" label="blacklist regioins" help="A pre-loaded list of blacklisted intervals.Refer to `selene` for details.">
+                    <option value="none" selected="true">None</option>
+                    <option value="hg38">hg38</option>
+                    <option value="hg19">hg19</option>
+                </param>
+                <param name="vcf_file" type="data" format="vcf" label="Dataset containing sequence variations" help="vcf"/>
+                <param name="seq_length" type="integer" value="1000" label="Encoding seqence length" help="A stretch of sequence surrounding the variation position on the reference genome."/>
+                <param name="output_reference" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Predict the reference sequence?" help="If False, predict on the variant sequence."/>
+            </when>
+        </conditional>
+    </inputs>
+    <outputs>
+        <data format="tabular" name="outfile_predict"/>
+    </outputs>
+    <tests>
+        <test>
+            <param name="infile_estimator" value="best_estimator_.zip" ftype="zip"/>
+            <param name="method" value="predict"/>
+            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
+            <param name="header1" value="true" />
+            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
+            <output name="outfile_predict" file="model_pred01.tabular"/>
+        </test>
+        <test>
+            <param name="infile_estimator" value="keras_model04" ftype="zip"/>
+            <param name="infile_weights" value="train_test_eval_weights02.h5" ftype="h5"/>
+            <param name="method" value="predict"/>
+            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
+            <param name="header1" value="true" />
+            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
+            <output name="outfile_predict" >
+                <assert_contents>
+                    <has_n_columns n="1"/>
+                    <has_text text="66.936"/>
+                    <has_text text="59.94"/>
+                    <has_text text="66.19"/>
+                    <has_text text="56.82"/>
+                    <has_text text="74.907"/>
+                </assert_contents>
+            </output>
+        </test>
+    </tests>
+    <help>
+        <![CDATA[
+**What it does**
+
+Given a fitted estimator and new data sets, this tool outpus the prediction results on the data sets via invoking the estimator's `predict` or `predict_proba` method.
+
+For estimator, this tool supports fitted sklearn estimators (pickled) and trained deep learning models (model skeleton + weights). It predicts on three different dataset inputs,
+
+- tabular
+
+- sparse
+
+- bio-sequences in a fasta file
+
+- reference genome and variant call file
+
+        ]]>
+    </help>
+    <expand macro="sklearn_citation">
+        <expand macro="keras_citation"/>
+        <expand macro="selene_citation"/>
+    </expand>
+</tool>
diff -r 000000000000 -r db511406350a search_model_validation.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/search_model_validation.py	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,572 @@
+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)
+
+    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_
+                del main_est.data_batch_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 db511406350a stacking_ensembles.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/stacking_ensembles.py	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,130 @@
+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:
+        options['weights'] = ast.literal_eval(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 db511406350a test-data/GridSearchCV.zip
Binary file test-data/GridSearchCV.zip has changed
diff -r 000000000000 -r db511406350a test-data/LinearRegression01.zip
Binary file test-data/LinearRegression01.zip has changed
diff -r 000000000000 -r db511406350a test-data/LinearRegression02.zip
Binary file test-data/LinearRegression02.zip has changed
diff -r 000000000000 -r db511406350a test-data/RF01704.fasta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/RF01704.fasta	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
+>CP000097.1/1411351-1411410
+CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA
+>ACNY01000002.1/278641-278580
+GAUCGUUCACUUCGCAUCGCGCGAAGCGCAGUUCGCCUCAGGCCAUGGAACGGGGACCUGAG
diff -r 000000000000 -r db511406350a test-data/RFE.zip
Binary file test-data/RFE.zip has changed
diff -r 000000000000 -r db511406350a test-data/RandomForestClassifier.zip
Binary file test-data/RandomForestClassifier.zip has changed
diff -r 000000000000 -r db511406350a test-data/RandomForestRegressor01.zip
Binary file test-data/RandomForestRegressor01.zip has changed
diff -r 000000000000 -r db511406350a test-data/StackingCVRegressor01.zip
Binary file test-data/StackingCVRegressor01.zip has changed
diff -r 000000000000 -r db511406350a test-data/StackingCVRegressor02.zip
Binary file test-data/StackingCVRegressor02.zip has changed
diff -r 000000000000 -r db511406350a test-data/StackingRegressor02.zip
Binary file test-data/StackingRegressor02.zip has changed
diff -r 000000000000 -r db511406350a test-data/StackingVoting03.zip
Binary file test-data/StackingVoting03.zip has changed
diff -r 000000000000 -r db511406350a test-data/XGBRegressor01.zip
Binary file test-data/XGBRegressor01.zip has changed
diff -r 000000000000 -r db511406350a test-data/abc_model01
Binary file test-data/abc_model01 has changed
diff -r 000000000000 -r db511406350a test-data/abc_result01
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/abc_result01	Fri Aug 09 07:11:11 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 db511406350a test-data/abr_model01
Binary file test-data/abr_model01 has changed
diff -r 000000000000 -r db511406350a test-data/abr_result01
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/abr_result01	Fri Aug 09 07:11:11 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 db511406350a test-data/accuracy_score.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/accuracy_score.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+accuracy_score : 
+0.8461538461538461
diff -r 000000000000 -r db511406350a test-data/auc.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/auc.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+auc : 
+2.5
diff -r 000000000000 -r db511406350a test-data/average_precision_score.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/average_precision_score.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+average_precision_score : 
+1.0
diff -r 000000000000 -r db511406350a test-data/best_estimator_.zip
Binary file test-data/best_estimator_.zip has changed
diff -r 000000000000 -r db511406350a test-data/best_params_.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/best_params_.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,1 @@
+{'estimator__n_estimators': 100}
\ No newline at end of file
diff -r 000000000000 -r db511406350a test-data/best_score_.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/best_score_.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+best_score_
+0.7976348550293088
diff -r 000000000000 -r db511406350a test-data/blobs.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/blobs.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,101 @@
+0	1	0
+0.33681845896740	-3.40287961299073	0
+-9.48324265575857	-8.66266051536995	2
+-1.93336328496076	5.70953908146890	1
+-10.03182405989413	-5.57834393458362	2
+0.54186077661701	-4.37693628326930	0
+-8.12962929067378	-7.05554320549807	2
+-0.73082578569427	7.32375551699482	1
+-1.84142532007015	6.20630466830832	1
+0.41007185031668	-3.99744881000119	0
+-8.73509589323240	-5.49090535208751	2
+1.84941962277054	-3.91839345672969	0
+-9.15256068848857	-9.17805648051067	2
+-3.21429939077830	5.75926163957071	1
+0.28450378549664	-3.61576522643830	0
+-0.92907484922306	5.79099955373578	1
+0.36692524194483	6.51861929622910	1
+1.59909917965412	-3.07105617297875	0
+-9.71270568435724	-7.91707651499009	2
+-10.08040443063205	-6.55135324108655	2
+1.10594345774293	-4.41906374949547	0
+2.48708049649457	-2.89100712361067	0
+0.00587148930883	-3.18314255539710	0
+1.61854359735349	-4.88855922559208	0
+-9.15856722108140	-7.13894114847511	2
+-3.07633571459573	7.80049676786476	1
+0.11174653022487	-3.61615828710479	0
+-9.43932350782336	-7.29863034570663	2
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diff -r 000000000000 -r db511406350a test-data/brier_score_loss.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/brier_score_loss.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+brier_score_loss : 
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diff -r 000000000000 -r db511406350a test-data/circles.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/circles.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/class.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/class.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/classification_report.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/classification_report.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,11 @@
+classification_report : 
+              precision    recall  f1-score   support
+
+           0       1.00      1.00      1.00        14
+           1       1.00      0.62      0.77        16
+           2       0.60      1.00      0.75         9
+
+   micro avg       0.85      0.85      0.85        39
+   macro avg       0.87      0.88      0.84        39
+weighted avg       0.91      0.85      0.85        39
+
diff -r 000000000000 -r db511406350a test-data/cluster_result01.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result01.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/cluster_result02.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result02.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/cluster_result03.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result03.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/cluster_result04.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result04.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/cluster_result05.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result05.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/cluster_result06.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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diff -r 000000000000 -r db511406350a test-data/cluster_result09.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result09.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result10.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result10.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result11.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result11.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result12.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result12.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result13.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result13.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result14.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result14.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result15.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result15.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result16.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result16.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result17.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result17.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result18.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result18.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result19.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result19.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result20.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result20.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
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diff -r 000000000000 -r db511406350a test-data/cluster_result21.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result21.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
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diff -r 000000000000 -r db511406350a test-data/confusion_matrix.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/confusion_matrix.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
+confusion_matrix : 
+[[14  0  0]
+ [ 0 10  6]
+ [ 0  0  9]]
diff -r 000000000000 -r db511406350a test-data/converter_result01.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result01.json	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+{"directed": false, "graph": {"info": "RNAfold", "id": "CP000097.1/1411351-1411410", "structure": "....((((.((((((....)))))).)..)))...(((((((..(...)..)).))))).", "sequence": "CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA"}, "nodes": [{"position": 0, "id": 0, "label": "C"}, {"position": 1, "id": 1, "label": "A"}, {"position": 2, "id": 2, "label": "A"}, {"position": 3, "id": 3, "label": "C"}, {"position": 4, "id": 4, "label": "G"}, {"position": 5, "id": 5, "label": "U"}, {"position": 6, "id": 6, "label": "U"}, {"position": 7, "id": 7, "label": "C"}, {"position": 8, "id": 8, "label": "A"}, {"position": 9, "id": 9, "label": "C"}, {"position": 10, "id": 10, "label": "C"}, {"position": 11, "id": 11, "label": "U"}, {"position": 12, "id": 12, "label": "C"}, {"position": 13, "id": 13, "label": "A"}, {"position": 14, "id": 14, "label": "C"}, {"position": 15, "id": 15, "label": "A"}, {"position": 16, "id": 16, "label": "U"}, {"position": 17, "id": 17, "label": "U"}, {"position": 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diff -r 000000000000 -r db511406350a test-data/converter_result02.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result02.json	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,4 @@
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diff -r 000000000000 -r db511406350a test-data/csc_sparse1.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_sparse1.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate integer general
+%
+3 3 6
+1 1 1
+3 1 2
+3 2 3
+1 3 4
+2 3 5
+3 3 6
diff -r 000000000000 -r db511406350a test-data/csc_sparse2.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_sparse2.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
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+2 3 0.1235
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diff -r 000000000000 -r db511406350a test-data/csc_stack_result01.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_stack_result01.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,15 @@
+%%MatrixMarket matrix coordinate real general
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diff -r 000000000000 -r db511406350a test-data/csr_sparse1.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_sparse1.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate integer general
+%
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diff -r 000000000000 -r db511406350a test-data/csr_sparse2.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_sparse2.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
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diff -r 000000000000 -r db511406350a test-data/csr_stack_result01.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_stack_result01.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,15 @@
+%%MatrixMarket matrix coordinate real general
+%
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diff -r 000000000000 -r db511406350a test-data/deepsear_1feature.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/deepsear_1feature.json	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,1 @@
+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Conv1D", "config": {"name": "conv1d_1", "trainable": true, "batch_input_shape": [null, 1000, 4], "dtype": "float32", "filters": 320, "kernel_size": [8], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "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}}, {"class_name": "MaxPooling1D", "config": {"name": "max_pooling1d_1", "trainable": true, "strides": [4], "pool_size": [4], "padding": "valid", "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.2, "noise_shape": null, "seed": 999}}, {"class_name": "Conv1D", "config": {"name": "conv1d_2", "trainable": true, "filters": 480, "kernel_size": [8], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "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}}, {"class_name": "MaxPooling1D", "config": {"name": "max_pooling1d_2", "trainable": true, "strides": [4], "pool_size": [4], "padding": "valid", "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "rate": 0.2, "noise_shape": null, "seed": 999}}, {"class_name": "Conv1D", "config": {"name": "conv1d_3", "trainable": true, "filters": 960, "kernel_size": [8], "strides": [1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1], "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}}, {"class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "rate": 0.5, "noise_shape": null, "seed": 999}}, {"class_name": "Reshape", "config": {"name": "reshape_1", "trainable": true, "target_shape": [50880]}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 1, "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}}, {"class_name": "Dense", "config": {"name": "dense_2", "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}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
diff -r 000000000000 -r db511406350a test-data/empty_file.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/empty_file.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,48 @@
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diff -r 000000000000 -r db511406350a test-data/f1_score.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/f1_score.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+f1_score : 
+0.8461538461538461
diff -r 000000000000 -r db511406350a test-data/fbeta_score.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/fbeta_score.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+fbeta_score : 
+0.8461538461538461
diff -r 000000000000 -r db511406350a test-data/feature_importances_.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_importances_.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,11 @@
+feature_importances_
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result01
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result01	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,11 @@
+0	1
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result02
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result02	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result03
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result03	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result04
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result04	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
+month	day	temp_2	temp_1	average	forecast_noaa	forecast_acc	forecast_under	friend	week_Mon
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+4.0	14.0	60.0	59.0	58.1	57.0	63.0	58.0	66.0	0.0
+7.0	30.0	85.0	88.0	77.3	75.0	79.0	77.0	70.0	0.0
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+12.0	31.0	48.0	57.0	45.5	42.0	48.0	47.0	57.0	0.0
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result05
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result05	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result06
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result06	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result07
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result07	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result08
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result08	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,11 @@
+0	1
+143.762620712	-0.330941870584
+-88.5787166225	1.08055532812
+-82.8452345578	0.272541389247
+72.4951388149	-0.26868660527800003
+11.805182128	1.0360467096600001
+-63.9354970901	-0.101485840571
+126.32584079600001	-0.35999834017899995
+23.0341392692	0.5185404651359999
+67.6714937696	-0.115688051547
+47.39275848810001	-0.7850965413680001
diff -r 000000000000 -r db511406350a test-data/feature_selection_result09
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result09	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,11 @@
+0
+143.762620712
+-88.5787166225
+-82.8452345578
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+11.805182128
+-63.9354970901
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result10
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result10	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
+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
+9.0	19.0	68.0	69.0	69.7	65.0	74.0	71.0	88.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0
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+7.0	30.0	85.0	88.0	77.3	75.0	79.0	77.0	70.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
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+1.0	18.0	54.0	50.0	47.5	44.0	48.0	49.0	58.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0
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+5.0	13.0	81.0	77.0	64.3	63.0	67.0	66.0	67.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
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+12.0	31.0	48.0	57.0	45.5	42.0	48.0	47.0	57.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
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+3.0	15.0	54.0	49.0	53.6	49.0	58.0	52.0	70.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
+4.0	19.0	77.0	89.0	59.0	59.0	63.0	59.0	61.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
+10.0	14.0	66.0	60.0	60.2	56.0	64.0	60.0	78.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
+4.0	15.0	59.0	59.0	58.3	58.0	61.0	60.0	40.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
diff -r 000000000000 -r db511406350a test-data/feature_selection_result11
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result11	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,51 @@
+Race	AIDS	Total
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+2.0	5788.0	2363908.0
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+3.0	4660.0	1630887.0
+3.0	5153.0	1845800.0
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diff -r 000000000000 -r db511406350a test-data/feature_selection_result12
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result12	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,11 @@
+0	1
+143.762620712	-0.330941870584
+-88.5787166225	1.08055532812
+-82.8452345578	0.272541389247
+72.4951388149	-0.26868660527800003
+11.805182128	1.0360467096600001
+-63.9354970901	-0.101485840571
+126.32584079600001	-0.35999834017899995
+23.0341392692	0.5185404651359999
+67.6714937696	-0.115688051547
+47.39275848810001	-0.7850965413680001
diff -r 000000000000 -r db511406350a test-data/feature_selection_result13
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_selection_result13	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/final_estimator.zip
Binary file test-data/final_estimator.zip has changed
diff -r 000000000000 -r db511406350a test-data/fitted_keras_g_regressor01.zip
Binary file test-data/fitted_keras_g_regressor01.zip has changed
diff -r 000000000000 -r db511406350a test-data/friedman1.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman1.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/friedman2.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman2.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r db511406350a test-data/friedman3.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman3.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r db511406350a test-data/gaus.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gaus.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/gbc_model01
Binary file test-data/gbc_model01 has changed
diff -r 000000000000 -r db511406350a test-data/gbc_result01
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbc_result01	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/gbr_model01
Binary file test-data/gbr_model01 has changed
diff -r 000000000000 -r db511406350a test-data/gbr_prediction_result01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbr_prediction_result01.tabular	Fri Aug 09 07:11:11 2019 -0400
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+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
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+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
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+2016	10	15	60	60	59.9	59	62	59	46	0	0	1	0	0	0	0	62.135403207035466
+2016	3	26	54	57	55.2	53	57	55	54	0	0	1	0	0	0	0	59.148716891180484
+2016	1	26	51	54	48.3	44	53	50	61	0	0	0	0	0	1	0	53.05069255536133
+2016	5	23	59	66	66.1	63	68	68	66	0	1	0	0	0	0	0	64.85734973368784
+2016	1	10	48	50	46.5	45	48	48	49	0	0	0	1	0	0	0	45.06961558051259
+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
+2016	1	23	52	57	48.0	45	49	50	37	0	0	1	0	0	0	0	50.836039030817304
+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
+2016	2	27	61	60	51.2	51	53	53	61	0	0	1	0	0	0	0	61.680080402280524
+2016	2	12	56	55	49.6	49	52	48	33	1	0	0	0	0	0	0	54.563346197441135
+2016	1	31	52	48	48.7	47	52	49	61	0	0	0	1	0	0	0	51.05906646453181
+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
+2016	12	6	46	40	46.4	44	50	45	56	0	0	0	0	0	1	0	42.363067913515295
+2016	12	21	46	51	45.1	44	50	46	39	0	0	0	0	0	0	1	45.6445314453422
+2016	1	4	44	41	45.9	44	48	46	53	0	1	0	0	0	0	0	42.214387828919136
+2016	10	2	67	63	64.9	62	69	66	82	0	0	0	1	0	0	0	62.736396078841445
+2016	5	28	65	64	66.8	64	69	65	64	0	0	1	0	0	0	0	63.947755881441275
+2016	9	11	74	77	72.1	69	75	71	70	0	0	0	1	0	0	0	73.98460722074996
+2016	10	25	62	61	56.5	53	60	55	70	0	0	0	0	0	1	0	61.917230159710556
+2016	2	18	56	57	50.1	47	55	49	34	0	0	0	0	1	0	0	55.720840480421955
+2016	11	1	117	59	54.5	51	59	55	61	0	0	0	0	0	1	0	61.52527009553642
+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
+2016	2	3	46	51	48.9	48	49	50	40	0	0	0	0	0	0	1	49.75042424691725
+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 db511406350a test-data/get_params.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params01.tabular	Fri Aug 09 07:11:11 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=<function f_classif at 0x111ef0158>)), ('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=<function f_classif at 0x111ef0158>)
+@	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: <function f_classif at 0x111ef0158>
+@	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 db511406350a test-data/get_params02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params02.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params03.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params03.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params04.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params04.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params05.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params05.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params06.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params06.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params07.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params07.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params08.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params08.tabular	Fri Aug 09 07:11:11 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=<function mean at 0x1123f1620>)), ('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=<function mean at 0x1123f1620>)"
+@	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: <function mean at 0x1123f1620>
+@	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 db511406350a test-data/get_params09.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params09.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params10.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params10.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params11.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params11.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/get_params12.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/get_params12.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/glm_model01
Binary file test-data/glm_model01 has changed
diff -r 000000000000 -r db511406350a test-data/glm_model02
Binary file test-data/glm_model02 has changed
diff -r 000000000000 -r db511406350a test-data/glm_model03
Binary file test-data/glm_model03 has changed
diff -r 000000000000 -r db511406350a test-data/glm_model04
Binary file test-data/glm_model04 has changed
diff -r 000000000000 -r db511406350a test-data/glm_model05
Binary file test-data/glm_model05 has changed
diff -r 000000000000 -r db511406350a test-data/glm_model06
Binary file test-data/glm_model06 has changed
diff -r 000000000000 -r db511406350a test-data/glm_model07
Binary file test-data/glm_model07 has changed
diff -r 000000000000 -r db511406350a test-data/glm_model08
Binary file test-data/glm_model08 has changed
diff -r 000000000000 -r db511406350a test-data/glm_result01
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result01	Fri Aug 09 07:11:11 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 db511406350a test-data/glm_result02
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result02	Fri Aug 09 07:11:11 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	0
+1.4132105208399999	0.523750660422	-1.4210539291	-1.49298569451	0
+0.7683140439399999	1.38267855169	-0.989045048734	0.649504257894	1
diff -r 000000000000 -r db511406350a test-data/glm_result03
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result03	Fri Aug 09 07:11:11 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	0
+1.4132105208399999	0.523750660422	-1.4210539291	-1.49298569451	0
+0.7683140439399999	1.38267855169	-0.989045048734	0.649504257894	1
diff -r 000000000000 -r db511406350a test-data/glm_result04
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result04	Fri Aug 09 07:11:11 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 db511406350a test-data/glm_result05
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result05	Fri Aug 09 07:11:11 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 db511406350a test-data/glm_result06
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result06	Fri Aug 09 07:11:11 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 db511406350a test-data/glm_result07
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result07	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,5 @@
+86.97021227350001	1.00532111569	-1.01739601979	-0.613139481654	0.641846874331	0.6093152833692663
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diff -r 000000000000 -r db511406350a test-data/glm_result08
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result08	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,5 @@
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diff -r 000000000000 -r db511406350a test-data/hamming_loss.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hamming_loss.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+hamming_loss : 
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diff -r 000000000000 -r db511406350a test-data/hastie.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hastie.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/hinge_loss.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hinge_loss.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+hinge_loss : 
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diff -r 000000000000 -r db511406350a test-data/imblearn_X.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/imblearn_X.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/imblearn_y.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/imblearn_y.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/jaccard_similarity_score.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/jaccard_similarity_score.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+jaccard_similarity_score : 
+0.8461538461538461
diff -r 000000000000 -r db511406350a test-data/keras01.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras01.json	Fri Aug 09 07:11:11 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 db511406350a test-data/keras02.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras02.json	Fri Aug 09 07:11:11 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": "linear", "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 db511406350a test-data/keras03.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras03.json	Fri Aug 09 07:11:11 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 db511406350a test-data/keras04.json
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras04.json	Fri Aug 09 07:11:11 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 db511406350a test-data/keras_batch_model01
Binary file test-data/keras_batch_model01 has changed
diff -r 000000000000 -r db511406350a test-data/keras_batch_model02
Binary file test-data/keras_batch_model02 has changed
diff -r 000000000000 -r db511406350a test-data/keras_batch_model03
Binary file test-data/keras_batch_model03 has changed
diff -r 000000000000 -r db511406350a test-data/keras_batch_params01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_batch_params01.tabular	Fri Aug 09 07:11:11 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 db511406350a test-data/keras_model01
Binary file test-data/keras_model01 has changed
diff -r 000000000000 -r db511406350a test-data/keras_model02
Binary file test-data/keras_model02 has changed
diff -r 000000000000 -r db511406350a test-data/keras_model04
Binary file test-data/keras_model04 has changed
diff -r 000000000000 -r db511406350a test-data/keras_params04.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_params04.tabular	Fri Aug 09 07:11:11 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.
diff -r 000000000000 -r db511406350a test-data/keras_prefitted01.zip
Binary file test-data/keras_prefitted01.zip has changed
diff -r 000000000000 -r db511406350a test-data/keras_save_weights01.h5
Binary file test-data/keras_save_weights01.h5 has changed
diff -r 000000000000 -r db511406350a test-data/lda_model01
Binary file test-data/lda_model01 has changed
diff -r 000000000000 -r db511406350a test-data/lda_model02
Binary file test-data/lda_model02 has changed
diff -r 000000000000 -r db511406350a test-data/lda_prediction_result01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/lda_prediction_result01.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,5 @@
+3.68258022948	2.82110345641	-3.990140724	-1.9523364774	0
+0.015942057224	-0.7119585943469999	0.125502976978	-0.972218263337	0
+2.0869076882499997	0.929399321468	-2.1292408448400004	-1.9971402218799998	0
+1.4132105208399999	0.523750660422	-1.4210539291	-1.49298569451	0
+0.7683140439399999	1.38267855169	-0.989045048734	0.649504257894	1
diff -r 000000000000 -r db511406350a test-data/lda_prediction_result02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/lda_prediction_result02.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,5 @@
+3.68258022948	2.82110345641	-3.990140724	-1.9523364774	0
+0.015942057224	-0.7119585943469999	0.125502976978	-0.972218263337	0
+2.0869076882499997	0.929399321468	-2.1292408448400004	-1.9971402218799998	0
+1.4132105208399999	0.523750660422	-1.4210539291	-1.49298569451	0
+0.7683140439399999	1.38267855169	-0.989045048734	0.649504257894	1
diff -r 000000000000 -r db511406350a test-data/log_loss.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/log_loss.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+log_loss : 
+3.7248735402728403
diff -r 000000000000 -r db511406350a test-data/matthews_corrcoef.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/matthews_corrcoef.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+matthews_corrcoef : 
+1.0
diff -r 000000000000 -r db511406350a test-data/model_pred01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/model_pred01.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
+Predicted
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diff -r 000000000000 -r db511406350a test-data/model_pred02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/model_pred02.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/moons.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/moons.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r db511406350a test-data/mv_result02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result02.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,11 @@
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diff -r 000000000000 -r db511406350a test-data/mv_result03.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result03.tabular	Fri Aug 09 07:11:11 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
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diff -r 000000000000 -r db511406350a test-data/mv_result05.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mv_result05.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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diff -r 000000000000 -r db511406350a test-data/named_steps.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/named_steps.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,6 @@
+{'preprocessing_1': SelectKBest(k=10, score_func=<function f_regression at 0x113310ea0>), '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=10,
+       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
+       silent=True, subsample=1)}
\ No newline at end of file
diff -r 000000000000 -r db511406350a test-data/nn_model01
Binary file test-data/nn_model01 has changed
diff -r 000000000000 -r db511406350a test-data/nn_model02
Binary file test-data/nn_model02 has changed
diff -r 000000000000 -r db511406350a test-data/nn_model03
Binary file test-data/nn_model03 has changed
diff -r 000000000000 -r db511406350a test-data/nn_prediction_result01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_prediction_result01.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/nn_prediction_result02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/nn_prediction_result02.tabular	Fri Aug 09 07:11:11 2019 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r db511406350a test-data/pickle_blacklist
--- /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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+0.20233065722424093	-0.37358807403702804	-0.01839561515890641	-0.8912230866367292	0.0
diff -r 000000000000 -r db511406350a test-data/prp_result05
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result05	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,7 @@
+%%MatrixMarket matrix coordinate real general
+%
+3 3 4
+1 1 1.000000000000000e+00
+2 3 1.000000000000000e+00
+3 1 1.000000000000000e+00
+3 3 1.000000000000000e+00
diff -r 000000000000 -r db511406350a test-data/prp_result06
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result06	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
+3 3 6
+1 1 1.000000000000000e+00
+3 1 4.000000000000000e-02
+3 2 -5.000000000000000e+00
+1 3 -2.000000000000000e-01
+2 3 1.100000000000000e+01
+3 3 2.600000000000000e+00
diff -r 000000000000 -r db511406350a test-data/prp_result07
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result07	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,15 @@
+0.10866513901130055	-0.5565683482001781	0.01697338750768846	-0.9589623671667038	-0.816496580927726
+-0.5710995365177258	0.07926796585473102	0.46542360804755395	0.9797654572160418	-0.816496580927726
+0.10847183700890116	0.6787364476581768	-0.2236147606088382	0.803743046654752	1.224744871391589
+1.9637931622636124	2.0890722453009336	-2.0606794162148767	0.00032423752699077795	1.224744871391589
+1.2351422905746392	1.2679619500798842	-1.2871193566513779	-0.06538948660301952	1.224744871391589
+0.8347358862676002	1.0161203164819261	-0.9008907216292501	0.18294534382616373	1.224744871391589
+-0.922721566734639	-0.28293538193724904	0.8320838514832234	0.9966141260199964	-0.816496580927726
+-0.7979810068833711	-1.9872356829362412	1.059205224122999	-1.6242152405020795	-0.816496580927726
+0.4693084330819043	0.8357250235474191	-0.5580390743243027	0.48013042183945476	1.224744871391589
+-2.1659119218220786	-1.284014236214121	2.073966413639728	1.4549796745789692	-0.816496580927726
+0.2160698816290759	-0.2483757987671466	-0.1335268595966537	-0.6822557426452339	-0.816496580927726
+-0.0027663810163240663	-0.35706357942460004	0.07191812706310458	-0.5052252645629531	-0.816496580927726
+0.5027769329398427	-0.20342998011241972	-0.3836970281346616	-1.053224520491157	-0.816496580927726
+-1.3239931073762934	-0.34533177433843787	1.182119596299028	1.5165437885484256	1.224744871391589
+0.3455099575735564	-0.7019291669926769	-0.15412299100336474	-1.5257734742396478	-0.816496580927726
diff -r 000000000000 -r db511406350a test-data/prp_result08
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result08	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
+3 3 6
+1 1 1.000000000000000e+00
+1 3 -1.818181818181818e-02
+2 3 1.000000000000000e+00
+3 1 4.000000000000000e-02
+3 2 -1.000000000000000e+00
+3 3 2.363636363636364e-01
diff -r 000000000000 -r db511406350a test-data/prp_result09
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result09	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
+3 3 6
+1 1 8.333333333333334e-01
+1 3 -1.666666666666667e-01
+2 3 1.000000000000000e+00
+3 1 5.235602094240837e-03
+3 2 -6.544502617801047e-01
+3 3 3.403141361256544e-01
diff -r 000000000000 -r db511406350a test-data/prp_result10
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/prp_result10	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
+year	month	day	temp_2	temp_1	average	forecast_noaa	forecast_acc	forecast_under	friend	week_Fri	week_Mon	week_Sat	week_Sun	week_Thurs	week_Tues	week_Wed
+-1.0	0.4545454545454546	0.19999999999999996	0.22222222222222188	-0.17073170731707288	0.5232198142414863	0.33333333333333304	0.6000000000000001	0.5428571428571427	0.791044776119403	-1.0	1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	-0.4545454545454546	-0.1333333333333333	-0.07407407407407396	-0.41463414634146334	-0.195046439628483	-0.11111111111111116	-0.02857142857142847	-0.20000000000000018	0.13432835820895517	-1.0	-1.0	-1.0	-1.0	1.0	-1.0	-1.0
+-1.0	0.09090909090909083	0.9333333333333333	0.8518518518518516	0.29268292682926855	0.9938080495356032	0.8888888888888884	0.8857142857142857	0.8857142857142852	0.25373134328358193	-1.0	-1.0	1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	-0.2727272727272727	-0.06666666666666665	0.7407407407407405	-0.26829268292682906	0.21362229102167207	0.22222222222222232	0.31428571428571406	0.1428571428571428	-0.10447761194029859	-1.0	-1.0	-1.0	1.0	-1.0	-1.0	-1.0
+-1.0	-1.0	0.1333333333333333	-0.2962962962962963	-0.6341463414634145	-0.8513931888544892	-0.8333333333333335	-0.8857142857142857	-0.7142857142857144	-0.10447761194029859	-1.0	1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	-1.0	0.6000000000000001	-0.5185185185185186	-0.6097560975609755	-0.8080495356037152	-0.7777777777777777	-0.7142857142857144	-0.7142857142857144	0.04477611940298498	-1.0	1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	0.8181818181818181	0.6000000000000001	-0.4814814814814816	-0.5853658536585364	-0.7832817337461302	-0.7777777777777777	-0.657142857142857	-0.8285714285714287	-0.6119402985074627	1.0	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	0.09090909090909083	0.2666666666666666	0.40740740740740744	0.048780487804878314	0.956656346749226	0.8888888888888884	0.8285714285714287	0.8857142857142852	0.13432835820895517	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0	1.0
+-1.0	0.9999999999999998	0.06666666666666665	-0.8518518518518519	-0.9999999999999999	-0.9938080495356036	-0.8888888888888888	-0.9428571428571431	-0.8857142857142857	-0.7014925373134329	-1.0	-1.0	1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	0.9999999999999998	-0.5333333333333333	-0.7407407407407409	-0.8780487804878048	-0.9380804953560373	-0.7777777777777777	-0.7142857142857144	-0.8285714285714287	-0.7611940298507462	-1.0	-1.0	-1.0	-1.0	1.0	-1.0	-1.0
+-1.0	0.9999999999999998	0.8	-0.7407407407407409	-0.7073170731707314	-0.9876160990712077	-1.0	-0.8285714285714287	-1.0	-0.10447761194029859	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0	1.0
+-1.0	0.09090909090909083	0.06666666666666665	0.5185185185185186	-0.09756097560975596	0.9318885448916405	0.9444444444444446	0.8285714285714287	0.8857142857142852	0.791044776119403	-1.0	-1.0	-1.0	1.0	-1.0	-1.0	-1.0
+-1.0	0.09090909090909083	-0.6	0.2592592592592591	2.220446049250313e-16	0.8142414860681115	0.7777777777777777	0.7714285714285709	0.7142857142857144	0.31343283582089554	-1.0	-1.0	-1.0	-1.0	1.0	-1.0	-1.0
+-1.0	0.9999999999999998	-0.06666666666666665	-0.8148148148148149	-0.9024390243902437	-0.9876160990712077	-0.7777777777777777	-0.8285714285714287	-0.8285714285714287	-0.4626865671641791	-1.0	-1.0	-1.0	-1.0	1.0	-1.0	-1.0
+-1.0	-0.09090909090909105	0.7333333333333334	0.33333333333333304	0.048780487804878314	0.6780185758513935	0.6111111111111112	0.6000000000000001	0.5999999999999996	0.6716417910447763	-1.0	1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	-0.2727272727272727	0.9999999999999998	0.07407407407407396	-0.12195121951219501	0.3746130030959747	0.22222222222222232	0.4857142857142853	0.37142857142857144	0.7014925373134326	-1.0	-1.0	-1.0	-1.0	-1.0	1.0	-1.0
+-1.0	-1.0	0.2666666666666666	-0.2962962962962963	-0.6829268292682924	-0.8390092879256965	-0.8333333333333335	-0.657142857142857	-0.7142857142857144	-0.014925373134328401	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0	1.0
+-1.0	0.2727272727272727	-0.4	0.40740740740740744	-0.09756097560975596	0.9752321981424141	1.0	0.8285714285714287	0.8857142857142852	0.19402985074626877	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0	1.0
+-1.0	-0.6363636363636365	0.46666666666666656	-0.22222222222222232	-0.4634146341463412	-0.40557275541795645	-0.5	-0.3142857142857145	-0.37142857142857144	0.25373134328358193	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0	1.0
+-1.0	0.9999999999999998	0.5333333333333334	-0.6296296296296298	-0.8780487804878048	-1.0	-0.8333333333333335	-0.9428571428571431	-0.8857142857142857	-0.6716417910447761	-1.0	-1.0	1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	-1.0	0.19999999999999996	-0.44444444444444464	-0.5365853658536583	-0.8452012383900929	-0.6666666666666665	-0.8285714285714287	-0.7714285714285714	-0.25373134328358216	-1.0	-1.0	-1.0	-1.0	-1.0	1.0	-1.0
+-1.0	0.8181818181818181	-0.6666666666666666	0.11111111111111116	-0.43902439024390216	-0.4984520123839009	-0.38888888888888884	-0.37142857142857144	-0.37142857142857144	0.28358208955223874	-1.0	-1.0	-1.0	1.0	-1.0	-1.0	-1.0
+-1.0	-0.4545454545454546	0.06666666666666665	-0.07407407407407396	-0.19512195121951192	-0.16408668730650167	-0.0555555555555558	-0.08571428571428585	-0.1428571428571428	-0.22388059701492535	-1.0	-1.0	-1.0	1.0	-1.0	-1.0	-1.0
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+-1.0	0.4545454545454546	0.9333333333333333	0.22222222222222188	-0.24390243902439002	0.27554179566563475	0.2777777777777777	0.19999999999999973	0.19999999999999973	0.37313432835820914	1.0	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	0.4545454545454546	-0.2666666666666666	0.5555555555555554	-0.14634146341463405	0.6532507739938072	0.4444444444444442	0.5428571428571431	0.6571428571428575	0.8507462686567162	-1.0	1.0	-1.0	-1.0	-1.0	-1.0	-1.0
+-1.0	0.8181818181818181	-0.9333333333333333	-0.11111111111111116	-0.4634146341463412	-0.43653250773993824	-0.2777777777777777	-0.3142857142857145	-0.37142857142857144	0.25373134328358193	-1.0	-1.0	-1.0	-1.0	-1.0	-1.0	1.0
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diff -r 000000000000 -r db511406350a test-data/pw_metric01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pw_metric01.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/pw_metric02.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pw_metric02.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/pw_metric03.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r db511406350a test-data/qda_model01
Binary file test-data/qda_model01 has changed
diff -r 000000000000 -r db511406350a test-data/qda_prediction_result01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/qda_prediction_result01.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/ranking_.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ranking_.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/recall_score.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/recall_score.txt	Fri Aug 09 07:11:11 2019 -0400
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+recall_score : 
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diff -r 000000000000 -r db511406350a test-data/regression.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression.txt	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/regression_X.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_X.tabular	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,262 @@
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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 db511406350a test-data/regression_test_y.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_test_y.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/regression_train.tabular
--- /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 db511406350a test-data/rfc_model01
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diff -r 000000000000 -r db511406350a test-data/rfc_result01
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/rfc_result01	Fri Aug 09 07:11:11 2019 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/rfc_result02	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/rfr_result01
--- /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 db511406350a test-data/roc_curve.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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diff -r 000000000000 -r db511406350a test-data/searchCV01
Binary file test-data/searchCV01 has changed
diff -r 000000000000 -r db511406350a test-data/searchCV02
Binary file test-data/searchCV02 has changed
diff -r 000000000000 -r db511406350a test-data/sparse.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/sparse.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,8741 @@
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diff -r 000000000000 -r db511406350a test-data/sparse_u.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/sparse_u.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r db511406350a test-data/test.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/test2.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test2.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/test3.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test3.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/test_set.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test_set.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/train.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/train_set.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_set.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/train_test_eval01.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_test_eval01.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/train_test_eval03.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_test_eval03.tabular	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/train_test_eval_model01
Binary file test-data/train_test_eval_model01 has changed
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diff -r 000000000000 -r db511406350a test-data/vectorizer_result01.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result01.mtx	Fri Aug 09 07:11:11 2019 -0400
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diff -r 000000000000 -r db511406350a test-data/vectorizer_result02.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result02.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,7346 @@
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diff -r 000000000000 -r db511406350a test-data/vectorizer_result03.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result03.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9697 @@
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diff -r 000000000000 -r db511406350a test-data/vectorizer_result04.mtx
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/vectorizer_result04.mtx	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,9697 @@
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diff -r 000000000000 -r db511406350a test-data/y.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/y.tabular	Fri Aug 09 07:11:11 2019 -0400
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+1	2	0.0	1.0	0.0	1	1	-1.88141734237	0.0471879612496	-0.990485600884
+1	2	0.0	0.9	0.1	1	1	-1.32547081613	-0.193430743286	-1.66958283068
+1	1	0.0	1.0	0.0	1	1	-2.7799666645	0.485621555351	1.21494093967
+2	2	0.0	0.2	0.8	1	1	-1.60125339649	-0.493901618129	-1.20213785254
+1	2	0.0	1.0	0.0	1	1	-1.86658623206	0.162709340336	-0.691875382528
+1	1	0.0	1.0	0.0	1	1	-1.82214550549	-0.130278514956	-0.836834994045
+1	2	0.0	0.9	0.1	1	1	-1.910728736	-0.0978509403157	-0.469743754594
+1	2	0.0	1.0	0.0	1	1	1.1191441248	-0.350015230403	-6.43122655533
+0	0	1.0	0.0	0.0	0	0	-1.80789829975	-0.267725170783	-0.533251833633
+1	1	0.0	0.9	0.1	1	1	-1.82704375852	0.186802710054	-0.367392242502
+1	1	0.0	0.9	0.1	1	1	1.05683832083	-0.491476736579	-6.10526049159
+0	0	1.0	0.0	0.0	0	0	1.58740583243	-1.32084852823	-7.47140590741
+0	0	1.0	0.0	0.0	0	0	-2.47802529094	-0.500673021108	1.37455405057
+2	2	0.0	0.3	0.7	1	1	-1.85517293032	-0.363363308535	-0.177124010926
+1	1	0.0	0.8	0.2	1	1	0.84169544958	-0.533176028466	-5.7625592501
+0	0	1.0	0.0	0.0	0	0	0.971871089969	-0.336154264594	-5.74291415928
+0	0	1.0	0.0	0.0	0	0	-2.18006328471	-0.33580204472	0.261632810716
+2	2	0.0	0.2	0.8	1	1	1.62753221054	-1.0437871236	-7.15189570944
+0	0	1.0	0.0	0.0	0	0	0.982418549211	-1.02370887933	-6.10073429813
+0	0	1.0	0.0	0.0	0	0	-1.51375235626	-0.156051081077	-1.37297970696
+1	1	0.0	1.0	0.0	1	1	-1.05517039337	0.171153321655	-1.66261211523
+1	1	0.0	1.0	0.0	1	1	1.05117238483	-0.819727602718	-6.16276877471
+0	0	1.0	0.0	0.0	0	0	-2.60008493281	-0.303483971372	0.937773514338
+2	2	0.0	0.0	1.0	1	1	-1.89873152969	-0.370955554274	0.0400346749524
+1	1	0.0	0.8	0.2	1	1	1.30185976049	-0.750494764082	-6.91956219185
+0	0	1.0	0.0	0.0	0	0	-2.20545858405	-0.462493064934	0.374957060793
+2	2	0.0	0.3	0.7	1	1	-2.97088391755	-0.384323906096	1.93410852068
+2	2	0.0	0.0	1.0	1	1	-1.52001848153	-0.275207915229	-0.625142611926
+1	1	0.0	1.0	0.0	1	1	1.32168915538	-0.986903615337	-7.22461895473
+0	0	1.0	0.0	0.0	0	0	-2.42938278814	0.0312031758068	0.740031884365
+1	2	0.0	0.0	1.0	1	1	-1.52001848153	-0.370955554274	0.937773514338
diff -r 000000000000 -r db511406350a test-data/zero_one_loss.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/zero_one_loss.txt	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,2 @@
+zero_one_loss : 
+0.15384615384615385
diff -r 000000000000 -r db511406350a train_test_eval.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/train_test_eval.py	Fri Aug 09 07:11:11 2019 -0400
@@ -0,0 +1,433 @@
+import argparse
+import joblib
+import json
+import numpy as np
+import pandas as pd
+import pickle
+import warnings
+from itertools import chain
+from scipy.io import mmread
+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.metrics.scorer import _check_multimetric_scoring
+from sklearn.model_selection._validation import _score, cross_validate
+from sklearn.model_selection import _search, _validation
+from sklearn.utils import indexable, safe_indexing
+
+from galaxy_ml.model_validations import train_test_split
+from galaxy_ml.utils import (SafeEval, 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_swap_params(params_builder):
+    swap_params = {}
+
+    for p in params_builder['param_set']:
+        swap_value = p['sp_value'].strip()
+        if swap_value == '':
+            continue
+
+        param_name = p['sp_name']
+        if param_name.lower().endswith(NON_SEARCHABLE):
+            warnings.warn("Warning: `%s` is not eligible for search and was "
+                          "omitted!" % param_name)
+            continue
+
+        if not swap_value.startswith(':'):
+            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
+            ev = safe_eval(swap_value)
+        else:
+            # Have `:` before search list, asks for estimator evaluatio
+            safe_eval_es = SafeEval(load_estimators=True)
+            swap_value = swap_value[1:].strip()
+            # TODO maybe add regular express check
+            ev = safe_eval_es(swap_value)
+
+        swap_params[param_name] = ev
+
+    return swap_params
+
+
+def train_test_split_none(*arrays, **kwargs):
+    """extend train_test_split to take None arrays
+    and support split by group names.
+    """
+    nones = []
+    new_arrays = []
+    for idx, arr in enumerate(arrays):
+        if arr is None:
+            nones.append(idx)
+        else:
+            new_arrays.append(arr)
+
+    if kwargs['shuffle'] == 'None':
+        kwargs['shuffle'] = None
+
+    group_names = kwargs.pop('group_names', None)
+
+    if group_names is not None and group_names.strip():
+        group_names = [name.strip() for name in
+                       group_names.split(',')]
+        new_arrays = indexable(*new_arrays)
+        groups = kwargs['labels']
+        n_samples = new_arrays[0].shape[0]
+        index_arr = np.arange(n_samples)
+        test = index_arr[np.isin(groups, group_names)]
+        train = index_arr[~np.isin(groups, group_names)]
+        rval = list(chain.from_iterable(
+            (safe_indexing(a, train),
+             safe_indexing(a, test)) for a in new_arrays))
+    else:
+        rval = train_test_split(*new_arrays, **kwargs)
+
+    for pos in nones:
+        rval[pos * 2: 2] = [None, None]
+
+    return rval
+
+
+def main(inputs, infile_estimator, infile1, infile2,
+         outfile_result, outfile_object=None,
+         outfile_weights=None, groups=None,
+         ref_seq=None, intervals=None, targets=None,
+         fasta_path=None):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : str
+        File path to estimator
+
+    infile1 : str
+        File path to dataset containing features
+
+    infile2 : str
+        File path to dataset containing target values
+
+    outfile_result : str
+        File path to save the results, either cv_results or test result
+
+    outfile_object : str, optional
+        File path to save searchCV object
+
+    outfile_weights : str, optional
+        File path to save deep learning model weights
+
+    groups : str
+        File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
+    """
+    warnings.simplefilter('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    #  load estimator
+    with open(infile_estimator, 'rb') as estimator_handler:
+        estimator = load_model(estimator_handler)
+
+    # swap hyperparameter
+    swapping = params['experiment_schemes']['hyperparams_swapping']
+    swap_params = _eval_swap_params(swapping)
+    estimator.set_params(**swap_params)
+
+    estimator_params = estimator.get_params()
+
+    # store read dataframe object
+    loaded_df = {}
+
+    input_type = params['input_options']['selected_input']
+    # tabular input
+    if input_type == 'tabular':
+        header = 'infer' if params['input_options']['header1'] else None
+        column_option = (params['input_options']['column_selector_options_1']
+                         ['selected_column_selector_option'])
+        if column_option in ['by_index_number', 'all_but_by_index_number',
+                             'by_header_name', 'all_but_by_header_name']:
+            c = params['input_options']['column_selector_options_1']['col1']
+        else:
+            c = None
+
+        df_key = infile1 + repr(header)
+        df = pd.read_csv(infile1, sep='\t', header=header,
+                         parse_dates=True)
+        loaded_df[df_key] = df
+
+        X = read_columns(df, c=c, c_option=column_option).astype(float)
+    # sparse input
+    elif input_type == 'sparse':
+        X = mmread(open(infile1, 'r'))
+
+    # fasta_file input
+    elif input_type == 'seq_fasta':
+        pyfaidx = get_module('pyfaidx')
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        for param in estimator_params.keys():
+            if param.endswith('fasta_path'):
+                estimator.set_params(
+                    **{param: fasta_path})
+                break
+        else:
+            raise ValueError(
+                "The selected estimator doesn't support "
+                "fasta file input! Please consider using "
+                "KerasGBatchClassifier with "
+                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+                "in pipeline!")
+
+    elif input_type == 'refseq_and_interval':
+        path_params = {
+            'data_batch_generator__ref_genome_path': ref_seq,
+            'data_batch_generator__intervals_path': intervals,
+            'data_batch_generator__target_path': targets
+        }
+        estimator.set_params(**path_params)
+        n_intervals = sum(1 for line in open(intervals))
+        X = np.arange(n_intervals)[:, np.newaxis]
+
+    # Get target y
+    header = 'infer' if params['input_options']['header2'] else None
+    column_option = (params['input_options']['column_selector_options_2']
+                     ['selected_column_selector_option2'])
+    if column_option in ['by_index_number', 'all_but_by_index_number',
+                         'by_header_name', 'all_but_by_header_name']:
+        c = params['input_options']['column_selector_options_2']['col2']
+    else:
+        c = None
+
+    df_key = infile2 + repr(header)
+    if df_key in loaded_df:
+        infile2 = loaded_df[df_key]
+    else:
+        infile2 = pd.read_csv(infile2, sep='\t',
+                              header=header, parse_dates=True)
+        loaded_df[df_key] = infile2
+
+    y = read_columns(
+            infile2,
+            c=c,
+            c_option=column_option,
+            sep='\t',
+            header=header,
+            parse_dates=True)
+    if len(y.shape) == 2 and y.shape[1] == 1:
+        y = y.ravel()
+    if input_type == 'refseq_and_interval':
+        estimator.set_params(
+            data_batch_generator__features=y.ravel().tolist())
+        y = None
+    # end y
+
+    # load groups
+    if groups:
+        groups_selector = (params['experiment_schemes']['test_split']
+                                 ['split_algos']).pop('groups_selector')
+
+        header = 'infer' if groups_selector['header_g'] else None
+        column_option = \
+            (groups_selector['column_selector_options_g']
+                            ['selected_column_selector_option_g'])
+        if column_option in ['by_index_number', 'all_but_by_index_number',
+                             'by_header_name', 'all_but_by_header_name']:
+            c = groups_selector['column_selector_options_g']['col_g']
+        else:
+            c = None
+
+        df_key = groups + repr(header)
+        if df_key in loaded_df:
+            groups = loaded_df[df_key]
+
+        groups = read_columns(
+                groups,
+                c=c,
+                c_option=column_option,
+                sep='\t',
+                header=header,
+                parse_dates=True)
+        groups = groups.ravel()
+
+    # del loaded_df
+    del loaded_df
+
+    # handle memory
+    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
+    # cache iraps_core fits could increase search speed significantly
+    if estimator.__class__.__name__ == 'IRAPSClassifier':
+        estimator.set_params(memory=memory)
+    else:
+        # For iraps buried in pipeline
+        new_params = {}
+        for p, v in estimator_params.items():
+            if p.endswith('memory'):
+                # for case of `__irapsclassifier__memory`
+                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
+                    # cache iraps_core fits could increase search
+                    # speed significantly
+                    new_params[p] = memory
+                # security reason, we don't want memory being
+                # modified unexpectedly
+                elif v:
+                    new_params[p] = None
+            # handle n_jobs
+            elif p.endswith('n_jobs'):
+                # For now, 1 CPU is suggested for iprasclassifier
+                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
+                    new_params[p] = 1
+                else:
+                    new_params[p] = N_JOBS
+            # for security reason, types of callback are limited
+            elif p.endswith('callbacks'):
+                for cb in v:
+                    cb_type = cb['callback_selection']['callback_type']
+                    if cb_type not in ALLOWED_CALLBACKS:
+                        raise ValueError(
+                            "Prohibited callback type: %s!" % cb_type)
+
+        estimator.set_params(**new_params)
+
+    # handle scorer, convert to scorer dict
+    scoring = params['experiment_schemes']['metrics']['scoring']
+    scorer = get_scoring(scoring)
+    scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+
+    # handle test (first) split
+    test_split_options = (params['experiment_schemes']
+                                ['test_split']['split_algos'])
+
+    if test_split_options['shuffle'] == 'group':
+        test_split_options['labels'] = groups
+    if test_split_options['shuffle'] == 'stratified':
+        if y is not None:
+            test_split_options['labels'] = y
+        else:
+            raise ValueError("Stratified shuffle split is not "
+                             "applicable on empty target values!")
+
+    X_train, X_test, y_train, y_test, groups_train, groups_test = \
+        train_test_split_none(X, y, groups, **test_split_options)
+
+    exp_scheme = params['experiment_schemes']['selected_exp_scheme']
+
+    # handle validation (second) split
+    if exp_scheme == 'train_val_test':
+        val_split_options = (params['experiment_schemes']
+                                   ['val_split']['split_algos'])
+
+        if val_split_options['shuffle'] == 'group':
+            val_split_options['labels'] = groups_train
+        if val_split_options['shuffle'] == 'stratified':
+            if y_train is not None:
+                val_split_options['labels'] = y_train
+            else:
+                raise ValueError("Stratified shuffle split is not "
+                                 "applicable on empty target values!")
+
+        X_train, X_val, y_train, y_val, groups_train, groups_val = \
+            train_test_split_none(X_train, y_train, groups_train,
+                                  **val_split_options)
+
+    # train and eval
+    if hasattr(estimator, 'validation_data'):
+        if exp_scheme == 'train_val_test':
+            estimator.fit(X_train, y_train,
+                          validation_data=(X_val, y_val))
+        else:
+            estimator.fit(X_train, y_train,
+                          validation_data=(X_test, y_test))
+    else:
+        estimator.fit(X_train, y_train)
+
+    if hasattr(estimator, 'evaluate'):
+        scores = estimator.evaluate(X_test, y_test=y_test,
+                                    scorer=scorer,
+                                    is_multimetric=True)
+    else:
+        scores = _score(estimator, X_test, y_test, scorer,
+                        is_multimetric=True)
+    # handle output
+    for name, score in scores.items():
+        scores[name] = [score]
+    df = pd.DataFrame(scores)
+    df = df[sorted(df.columns)]
+    df.to_csv(path_or_buf=outfile_result, sep='\t',
+              header=True, index=False)
+
+    memory.clear(warn=False)
+
+    if outfile_object:
+        main_est = estimator
+        if isinstance(estimator, pipeline.Pipeline):
+            main_est = estimator.steps[-1][-1]
+
+        if hasattr(main_est, 'model_') \
+                and hasattr(main_est, 'save_weights'):
+            if outfile_weights:
+                main_est.save_weights(outfile_weights)
+            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_
+                del main_est.data_batch_generator
+
+        with open(outfile_object, 'wb') as output_handler:
+            pickle.dump(estimator, output_handler,
+                        pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
+    aparser.add_argument("-X", "--infile1", dest="infile1")
+    aparser.add_argument("-y", "--infile2", dest="infile2")
+    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
+    aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
+         args.outfile_result, outfile_object=args.outfile_object,
+         outfile_weights=args.outfile_weights, groups=args.groups,
+         ref_seq=args.ref_seq, intervals=args.intervals,
+         targets=args.targets, fasta_path=args.fasta_path)