Repository 'sklearn_svm_classifier'
hg clone https://toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_svm_classifier

Changeset 15:2df8f5c30edc (2019-12-16)
Previous changeset 14:da0a22cdcce7 (2019-11-07) Next changeset 16:f9639b488779 (2020-01-22)
Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
modified:
keras_deep_learning.py
keras_macros.xml
main_macros.xml
ml_visualization_ex.py
model_prediction.py
search_model_validation.py
svm.xml
test-data/RandomForestClassifier.zip
test-data/StackingCVRegressor01.zip
test-data/StackingRegressor02.zip
test-data/StackingVoting03.zip
test-data/abc_model01
test-data/abr_model01
test-data/best_estimator_.zip
test-data/brier_score_loss.txt
test-data/classification_report.txt
test-data/gbc_model01
test-data/gbr_model01
test-data/get_params05.tabular
test-data/get_params12.tabular
test-data/glm_model01
test-data/glm_model02
test-data/glm_model03
test-data/glm_model04
test-data/glm_model05
test-data/glm_model06
test-data/glm_model07
test-data/glm_model08
test-data/glm_result01
test-data/glm_result02
test-data/glm_result08
test-data/keras01.json
test-data/keras02.json
test-data/keras04.json
test-data/keras_batch_model01
test-data/keras_batch_model02
test-data/keras_batch_params01.tabular
test-data/keras_model01
test-data/keras_model02
test-data/keras_model04
test-data/keras_params04.tabular
test-data/keras_prefitted01.zip
test-data/keras_save_weights01.h5
test-data/lda_model01
test-data/lda_model02
test-data/ml_vis01.html
test-data/ml_vis02.html
test-data/ml_vis03.html
test-data/ml_vis04.html
test-data/ml_vis05.html
test-data/ml_vis05.png
test-data/model_fit01
test-data/model_fit02
test-data/model_fit02.h5
test-data/named_steps.txt
test-data/nn_model01
test-data/nn_model02
test-data/nn_model03
test-data/pipeline01
test-data/pipeline02
test-data/pipeline03
test-data/pipeline04
test-data/pipeline05
test-data/pipeline06
test-data/pipeline07
test-data/pipeline08
test-data/pipeline09
test-data/pipeline10
test-data/pipeline11
test-data/pipeline12
test-data/pipeline14
test-data/pipeline15
test-data/pipeline16
test-data/prp_model03
test-data/prp_model05
test-data/prp_model08
test-data/prp_model09
test-data/qda_model01
test-data/rfc_model01
test-data/rfr_model01
test-data/searchCV01
test-data/searchCV02
test-data/train_test_eval_model01
test-data/train_test_eval_weights01.h5
test-data/train_test_eval_weights02.h5
train_test_eval.py
added:
keras_train_and_eval.py
test-data/keras_batch_model04
test-data/keras_batch_params04.tabular
test-data/keras_train_eval_y_true02.tabular
test-data/pipeline17
test-data/pipeline_params05.tabular
test-data/pipeline_params18
b
diff -r da0a22cdcce7 -r 2df8f5c30edc keras_deep_learning.py
--- a/keras_deep_learning.py Thu Nov 07 05:31:04 2019 -0500
+++ b/keras_deep_learning.py Mon Dec 16 05:21:05 2019 -0500
[
@@ -73,7 +73,7 @@
             }
     """
     constraint_type = config['constraint_type']
-    if constraint_type == 'None':
+    if constraint_type in ('None', ''):
         return None
 
     klass = getattr(keras.constraints, constraint_type)
@@ -92,7 +92,7 @@
     """Access to handle all kinds of parameters
     """
     for key, value in six.iteritems(params):
-        if value == 'None':
+        if value in ('None', ''):
             params[key] = None
             continue
 
@@ -205,6 +205,9 @@
     config : dictionary, galaxy tool parameters loaded by JSON
     """
     generator_type = config.pop('generator_type')
+    if generator_type == 'none':
+        return None
+
     klass = try_get_attr('galaxy_ml.preprocessors', generator_type)
 
     if generator_type == 'GenomicIntervalBatchGenerator':
@@ -240,7 +243,7 @@
     json_string = model.to_json()
 
     with open(outfile, 'w') as f:
-        f.write(json_string)
+        json.dump(json.loads(json_string), f, indent=2)
 
 
 def build_keras_model(inputs, outfile, model_json, infile_weights=None,
b
diff -r da0a22cdcce7 -r 2df8f5c30edc keras_macros.xml
--- a/keras_macros.xml Thu Nov 07 05:31:04 2019 -0500
+++ b/keras_macros.xml Mon Dec 16 05:21:05 2019 -0500
b
@@ -1,5 +1,5 @@
 <macros>
-  <token name="@KERAS_VERSION@">0.4.2</token>
+  <token name="@KERAS_VERSION@">0.5.0</token>
 
   <xml name="macro_stdio">
     <stdio>
@@ -18,7 +18,7 @@
 
   <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="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>
@@ -885,7 +885,7 @@
           <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="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>
b
diff -r da0a22cdcce7 -r 2df8f5c30edc keras_train_and_eval.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_train_and_eval.py Mon Dec 16 05:21:05 2019 -0500
[
b'@@ -0,0 +1,491 @@\n+import argparse\n+import joblib\n+import json\n+import numpy as np\n+import os\n+import pandas as pd\n+import pickle\n+import warnings\n+from itertools import chain\n+from scipy.io import mmread\n+from sklearn.pipeline import Pipeline\n+from sklearn.metrics.scorer import _check_multimetric_scoring\n+from sklearn import model_selection\n+from sklearn.model_selection._validation import _score\n+from sklearn.model_selection import _search, _validation\n+from sklearn.utils import indexable, safe_indexing\n+\n+from galaxy_ml.externals.selene_sdk.utils import compute_score\n+from galaxy_ml.model_validations import train_test_split\n+from galaxy_ml.keras_galaxy_models import _predict_generator\n+from galaxy_ml.utils import (SafeEval, get_scoring, load_model,\n+                             read_columns, try_get_attr, get_module,\n+                             clean_params, get_main_estimator)\n+\n+\n+_fit_and_score = try_get_attr(\'galaxy_ml.model_validations\', \'_fit_and_score\')\n+setattr(_search, \'_fit_and_score\', _fit_and_score)\n+setattr(_validation, \'_fit_and_score\', _fit_and_score)\n+\n+N_JOBS = int(os.environ.get(\'GALAXY_SLOTS\', 1))\n+CACHE_DIR = os.path.join(os.getcwd(), \'cached\')\n+del os\n+NON_SEARCHABLE = (\'n_jobs\', \'pre_dispatch\', \'memory\', \'_path\',\n+                  \'nthread\', \'callbacks\')\n+ALLOWED_CALLBACKS = (\'EarlyStopping\', \'TerminateOnNaN\', \'ReduceLROnPlateau\',\n+                     \'CSVLogger\', \'None\')\n+\n+\n+def _eval_swap_params(params_builder):\n+    swap_params = {}\n+\n+    for p in params_builder[\'param_set\']:\n+        swap_value = p[\'sp_value\'].strip()\n+        if swap_value == \'\':\n+            continue\n+\n+        param_name = p[\'sp_name\']\n+        if param_name.lower().endswith(NON_SEARCHABLE):\n+            warnings.warn("Warning: `%s` is not eligible for search and was "\n+                          "omitted!" % param_name)\n+            continue\n+\n+        if not swap_value.startswith(\':\'):\n+            safe_eval = SafeEval(load_scipy=True, load_numpy=True)\n+            ev = safe_eval(swap_value)\n+        else:\n+            # Have `:` before search list, asks for estimator evaluatio\n+            safe_eval_es = SafeEval(load_estimators=True)\n+            swap_value = swap_value[1:].strip()\n+            # TODO maybe add regular express check\n+            ev = safe_eval_es(swap_value)\n+\n+        swap_params[param_name] = ev\n+\n+    return swap_params\n+\n+\n+def train_test_split_none(*arrays, **kwargs):\n+    """extend train_test_split to take None arrays\n+    and support split by group names.\n+    """\n+    nones = []\n+    new_arrays = []\n+    for idx, arr in enumerate(arrays):\n+        if arr is None:\n+            nones.append(idx)\n+        else:\n+            new_arrays.append(arr)\n+\n+    if kwargs[\'shuffle\'] == \'None\':\n+        kwargs[\'shuffle\'] = None\n+\n+    group_names = kwargs.pop(\'group_names\', None)\n+\n+    if group_names is not None and group_names.strip():\n+        group_names = [name.strip() for name in\n+                       group_names.split(\',\')]\n+        new_arrays = indexable(*new_arrays)\n+        groups = kwargs[\'labels\']\n+        n_samples = new_arrays[0].shape[0]\n+        index_arr = np.arange(n_samples)\n+        test = index_arr[np.isin(groups, group_names)]\n+        train = index_arr[~np.isin(groups, group_names)]\n+        rval = list(chain.from_iterable(\n+            (safe_indexing(a, train),\n+             safe_indexing(a, test)) for a in new_arrays))\n+    else:\n+        rval = train_test_split(*new_arrays, **kwargs)\n+\n+    for pos in nones:\n+        rval[pos * 2: 2] = [None, None]\n+\n+    return rval\n+\n+\n+def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):\n+    """ output scores based on input scorer\n+\n+    Parameters\n+    ----------\n+    y_true : array\n+        True label or target values\n+    pred_probas : array\n+        Prediction values, probability for classification problem\n+    scorer : dict\n+        dict of `sklearn.metrics.scorer.SCORER`\n+    is_multimetric : bool, default is True\n+    """\n+ '..b'          validation_data=(X_val, y_val))\n+        else:\n+            estimator.fit(X_train, y_train,\n+                          validation_data=(X_test, y_test))\n+    else:\n+        estimator.fit(X_train, y_train)\n+\n+    if hasattr(estimator, \'evaluate\'):\n+        steps = estimator.prediction_steps\n+        batch_size = estimator.batch_size\n+        generator = estimator.data_generator_.flow(X_test, y=y_test,\n+                                                   batch_size=batch_size)\n+        predictions, y_true = _predict_generator(estimator.model_, generator,\n+                                                 steps=steps)\n+        scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)\n+\n+    else:\n+        if hasattr(estimator, \'predict_proba\'):\n+            predictions = estimator.predict_proba(X_test)\n+        else:\n+            predictions = estimator.predict(X_test)\n+\n+        y_true = y_test\n+        scores = _score(estimator, X_test, y_test, scorer,\n+                        is_multimetric=True)\n+    if outfile_y_true:\n+        try:\n+            pd.DataFrame(y_true).to_csv(outfile_y_true, sep=\'\\t\',\n+                                        index=False)\n+            pd.DataFrame(predictions).astype(np.float32).to_csv(\n+                outfile_y_preds, sep=\'\\t\', index=False,\n+                float_format=\'%g\', chunksize=10000)\n+        except Exception as e:\n+            print("Error in saving predictions: %s" % e)\n+\n+    # handle output\n+    for name, score in scores.items():\n+        scores[name] = [score]\n+    df = pd.DataFrame(scores)\n+    df = df[sorted(df.columns)]\n+    df.to_csv(path_or_buf=outfile_result, sep=\'\\t\',\n+              header=True, index=False)\n+\n+    memory.clear(warn=False)\n+\n+    if outfile_object:\n+        main_est = estimator\n+        if isinstance(estimator, Pipeline):\n+            main_est = estimator.steps[-1][-1]\n+\n+        if hasattr(main_est, \'model_\') \\\n+                and hasattr(main_est, \'save_weights\'):\n+            if outfile_weights:\n+                main_est.save_weights(outfile_weights)\n+            del main_est.model_\n+            del main_est.fit_params\n+            del main_est.model_class_\n+            del main_est.validation_data\n+            if getattr(main_est, \'data_generator_\', None):\n+                del main_est.data_generator_\n+\n+        with open(outfile_object, \'wb\') as output_handler:\n+            pickle.dump(estimator, output_handler,\n+                        pickle.HIGHEST_PROTOCOL)\n+\n+\n+if __name__ == \'__main__\':\n+    aparser = argparse.ArgumentParser()\n+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)\n+    aparser.add_argument("-e", "--estimator", dest="infile_estimator")\n+    aparser.add_argument("-X", "--infile1", dest="infile1")\n+    aparser.add_argument("-y", "--infile2", dest="infile2")\n+    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")\n+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")\n+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")\n+    aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")\n+    aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")\n+    aparser.add_argument("-g", "--groups", dest="groups")\n+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")\n+    aparser.add_argument("-b", "--intervals", dest="intervals")\n+    aparser.add_argument("-t", "--targets", dest="targets")\n+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")\n+    args = aparser.parse_args()\n+\n+    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,\n+         args.outfile_result, outfile_object=args.outfile_object,\n+         outfile_weights=args.outfile_weights,\n+         outfile_y_true=args.outfile_y_true,\n+         outfile_y_preds=args.outfile_y_preds,\n+         groups=args.groups,\n+         ref_seq=args.ref_seq, intervals=args.intervals,\n+         targets=args.targets, fasta_path=args.fasta_path)\n'
b
diff -r da0a22cdcce7 -r 2df8f5c30edc main_macros.xml
--- a/main_macros.xml Thu Nov 07 05:31:04 2019 -0500
+++ b/main_macros.xml Mon Dec 16 05:21:05 2019 -0500
[
b'@@ -1,12 +1,10 @@\n <macros>\n-  <token name="@VERSION@">1.0.7.12</token>\n-\n-  <token name="@ENSEMBLE_VERSION@">0.2.0</token>\n+  <token name="@VERSION@">1.0.8.1</token>\n \n   <xml name="python_requirements">\n       <requirements>\n           <requirement type="package" version="3.6">python</requirement>\n-          <requirement type="package" version="0.7.12">Galaxy-ML</requirement>\n+          <requirement type="package" version="0.8.1">Galaxy-ML</requirement>\n           <yield/>\n       </requirements>\n   </xml>\n@@ -235,8 +233,8 @@\n     <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."/>\n   </xml>\n \n-  <xml name="n_iter" token_default_value="5" token_help_text="The number of passes over the training data (aka epochs). ">\n-    <param argument="n_iter" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/>\n+  <xml name="n_iter_no_change" token_default_value="5" token_help_text="Number of iterations with no improvement to wait before early stopping. ">\n+    <param argument="n_iter_no_change" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/>\n   </xml>\n \n   <xml name="shuffle" token_checked="true" token_help_text=" " token_label="Shuffle data after each iteration">\n@@ -763,6 +761,9 @@\n       <option value="MinMaxScaler">Minmax Scaler (Scales features to a range)</option>\n       <option value="PolynomialFeatures">Polynomial Features (Generates polynomial and interaction features)</option>\n       <option value="RobustScaler">Robust Scaler (Scales features using outlier-invariance statistics)</option>\n+      <option value="QuantileTransformer">QuantileTransformer (Transform features using quantiles information)</option>\n+      <option value="PowerTransformer">PowerTransformer (Apply a power transform featurewise to make data more Gaussian-like)</option>\n+      <option value="KBinsDiscretizer">KBinsDiscretizer (Bin continuous data into intervals.)</option>\n     </expand>\n   </xml>\n \n@@ -837,6 +838,42 @@\n                   label="Use a copy of data for inplace scaling" help=" "/>\n           </section>\n       </when>\n+      <when value="QuantileTransformer">\n+          <section name="options" title="Advanced Options" expanded="False">\n+              <param name="n_quantiles" type="integer" value="1000" min="0" label="Number of quantiles to be computed" />\n+              <param name="output_distribution" type="select" label="Marginal distribution for the transformed data">\n+                  <option value="uniform" selected="true">uniform</option>\n+                  <option value="normal">normal</option>\n+              </param>\n+              <param name="ignore_implicit_zeros" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to discard sparse entries" help="Only applies to sparse matrices. If False, sparse entries are treated as zeros"/>\n+              <param name="subsample" type="integer" value="100000" label="Maximum number of samples used to estimate the quantiles for computational efficiency" help="Note that the subsampling procedure may differ for value-identical sparse and dense matrices."/>\n+              <expand macro="random_state" help_text="This is used by subsampling and smoothing noise"/>\n+          </section>\n+      </when>\n+      <when value="PowerTransformer">\n+          <section name="options" title="Advanced Options" expanded="False">\n+              <param name="method" type="select" label="The power transform method">\n+                  <option value="yeo-johnson" selected="true">yeo-johnson (works with positive and negative values)</option>\n+                  <option value="box-cox">box-cox (might perform better, but only works with strictly positive values)</option>\n+              </param>\n+              <param'..b're_dispatch" help="@HELP@"/>\n   </xml>\n \n-  <xml name="search_cv_estimator">\n-    <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>\n-    <section name="search_params_builder" title="Search parameters Builder" expanded="true">\n-      <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."/>\n-      <repeat name="param_set" min="1" max="30" title="Parameter settings for search:">\n-          <param name="sp_name" type="select" optional="true" label="Choose a parameter name (with current value)">\n-            <options from_dataset="infile_params" startswith="@">\n-              <column name="name" index="2"/>\n-              <column name="value" index="1"/>\n-              <filter type="unique_value" name="unique_param" column="1"/>\n-            </options>\n-          </param>\n-          <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">\n-            <sanitizer>\n-              <valid initial="default">\n-                <add value="&apos;"/>\n-                <add value="&quot;"/>\n-                <add value="["/>\n-                <add value="]"/>\n-              </valid>\n-            </sanitizer>\n-          </param>\n-      </repeat>\n-    </section>\n-  </xml>\n-\n   <xml name="estimator_and_hyperparameter">\n     <param name="infile_estimator" type="data" format="zip" label="Choose the dataset containing pipeline/estimator object"/>\n     <section name="hyperparams_swapping" title="Hyperparameter Swapping" expanded="false">\n@@ -1398,7 +1412,7 @@\n       <expand macro="model_validation_common_options"/>\n       <!--expand macro="pre_dispatch" default_value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/-->\n       <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="If True, data is identically distributed across the folds"/>\n-      <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. Be aware that `refit=True` invokes extra computation, but it\'s REQUIRED for outputting the best estimator!"/>\n+      <!--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. Be aware that `refit=True` invokes extra computation, but it\'s REQUIRED for outputting the best estimator!"/> -->\n       <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."/>\n       <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/>\n   </xml>\n@@ -1475,6 +1489,8 @@\n           <option value="GradientBoostingClassifier">GradientBoostingClassifier</option>\n           <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>\n           <option value="IsolationForest">IsolationForest</option>\n+          <option value="HistGradientBoostingClassifier">HistGradientBoostingClassifier</option>\n+          <option value="HistGradientBoostingRegressor">HistGradientBoostingRegressor</option>\n           <option value="RandomForestClassifier">RandomForestClassifier</option>\n           <option value="RandomForestRegressor">RandomForestRegressor</option>\n           <option value="RandomTreesEmbedding">RandomTreesEmbedding</option>\n'
b
diff -r da0a22cdcce7 -r 2df8f5c30edc ml_visualization_ex.py
--- a/ml_visualization_ex.py Thu Nov 07 05:31:04 2019 -0500
+++ b/ml_visualization_ex.py Mon Dec 16 05:21:05 2019 -0500
[
b'@@ -1,6 +1,9 @@\n import argparse\n import json\n+import matplotlib\n+import matplotlib.pyplot as plt\n import numpy as np\n+import os\n import pandas as pd\n import plotly\n import plotly.graph_objs as go\n@@ -17,6 +20,251 @@\n \n safe_eval = SafeEval()\n \n+# plotly default colors\n+default_colors = [\n+    \'#1f77b4\',  # muted blue\n+    \'#ff7f0e\',  # safety orange\n+    \'#2ca02c\',  # cooked asparagus green\n+    \'#d62728\',  # brick red\n+    \'#9467bd\',  # muted purple\n+    \'#8c564b\',  # chestnut brown\n+    \'#e377c2\',  # raspberry yogurt pink\n+    \'#7f7f7f\',  # middle gray\n+    \'#bcbd22\',  # curry yellow-green\n+    \'#17becf\'   # blue-teal\n+]\n+\n+\n+def visualize_pr_curve_plotly(df1, df2, pos_label, title=None):\n+    """output pr-curve in html using plotly\n+\n+    df1 : pandas.DataFrame\n+        Containing y_true\n+    df2 : pandas.DataFrame\n+        Containing y_score\n+    pos_label : None\n+        The label of positive class\n+    title : str\n+        Plot title\n+    """\n+    data = []\n+    for idx in range(df1.shape[1]):\n+        y_true = df1.iloc[:, idx].values\n+        y_score = df2.iloc[:, idx].values\n+\n+        precision, recall, _ = precision_recall_curve(\n+            y_true, y_score, pos_label=pos_label)\n+        ap = average_precision_score(\n+            y_true, y_score, pos_label=pos_label or 1)\n+\n+        trace = go.Scatter(\n+            x=recall,\n+            y=precision,\n+            mode=\'lines\',\n+            marker=dict(\n+                color=default_colors[idx % len(default_colors)]\n+            ),\n+            name=\'%s (area = %.3f)\' % (idx, ap)\n+        )\n+        data.append(trace)\n+\n+    layout = go.Layout(\n+        xaxis=dict(\n+            title=\'Recall\',\n+            linecolor=\'lightslategray\',\n+            linewidth=1\n+        ),\n+        yaxis=dict(\n+            title=\'Precision\',\n+            linecolor=\'lightslategray\',\n+            linewidth=1\n+        ),\n+        title=dict(\n+            text=title or \'Precision-Recall Curve\',\n+            x=0.5,\n+            y=0.92,\n+            xanchor=\'center\',\n+            yanchor=\'top\'\n+        ),\n+        font=dict(\n+            family="sans-serif",\n+            size=11\n+        ),\n+        # control backgroud colors\n+        plot_bgcolor=\'rgba(255,255,255,0)\'\n+    )\n+    """\n+    legend=dict(\n+        x=0.95,\n+        y=0,\n+        traceorder="normal",\n+        font=dict(\n+            family="sans-serif",\n+            size=9,\n+            color="black"\n+        ),\n+        bgcolor="LightSteelBlue",\n+        bordercolor="Black",\n+        borderwidth=2\n+    ),"""\n+\n+    fig = go.Figure(data=data, layout=layout)\n+\n+    plotly.offline.plot(fig, filename="output.html", auto_open=False)\n+    # to be discovered by `from_work_dir`\n+    os.rename(\'output.html\', \'output\')\n+\n+\n+def visualize_pr_curve_matplotlib(df1, df2, pos_label, title=None):\n+    """visualize pr-curve using matplotlib and output svg image\n+    """\n+    backend = matplotlib.get_backend()\n+    if "inline" not in backend:\n+        matplotlib.use("SVG")\n+    plt.style.use(\'seaborn-colorblind\')\n+    plt.figure()\n+\n+    for idx in range(df1.shape[1]):\n+        y_true = df1.iloc[:, idx].values\n+        y_score = df2.iloc[:, idx].values\n+\n+        precision, recall, _ = precision_recall_curve(\n+            y_true, y_score, pos_label=pos_label)\n+        ap = average_precision_score(\n+            y_true, y_score, pos_label=pos_label or 1)\n+\n+        plt.step(recall, precision, \'r-\', color="black", alpha=0.3,\n+                 lw=1, where="post", label=\'%s (area = %.3f)\' % (idx, ap))\n+\n+    plt.xlim([0.0, 1.0])\n+    plt.ylim([0.0, 1.05])\n+    plt.xlabel(\'Recall\')\n+    plt.ylabel(\'Precision\')\n+    title = title or \'Precision-Recall Curve\'\n+    plt.title(title)\n+    folder = os.getcwd()\n+    plt.savefig(os.path.join(folder, "output.svg"), format="svg")\n+    os.rename(os.path.join(folder, "output.svg"),\n+              os.path.join(folder, "output"))\n+\n+\n+def visualize_roc_curve_plotly(df1, df2, pos_label,\n+                            '..b'             line=dict(color=\'black\', dash=\'dash\'),\n-                           showlegend=False)\n-        data.append(trace)\n-\n-        layout = go.Layout(\n-            title=title or "Receiver operating characteristic curve",\n-            xaxis=dict(title=\'False Positive Rate\'),\n-            yaxis=dict(title=\'True Positive Rate\')\n-        )\n-\n-        fig = go.Figure(data=data, layout=layout)\n+        return 0\n \n     elif plot_type == \'rfecv_gridscores\':\n         input_df = pd.read_csv(infile1, sep=\'\\t\', header=\'infer\')\n@@ -231,10 +429,43 @@\n         layout = go.Layout(\n             xaxis=dict(title="Number of features selected"),\n             yaxis=dict(title="Cross validation score"),\n-            title=title or None\n+            title=dict(\n+                text=title or None,\n+                x=0.5,\n+                y=0.92,\n+                xanchor=\'center\',\n+                yanchor=\'top\'\n+            ),\n+            font=dict(\n+                family="sans-serif",\n+                size=11\n+            ),\n+            # control backgroud colors\n+            plot_bgcolor=\'rgba(255,255,255,0)\'\n         )\n+        """\n+        # legend=dict(\n+                # x=0.95,\n+                # y=0,\n+                # traceorder="normal",\n+                # font=dict(\n+                #    family="sans-serif",\n+                #    size=9,\n+                #    color="black"\n+                # ),\n+                # bgcolor="LightSteelBlue",\n+                # bordercolor="Black",\n+                # borderwidth=2\n+            # ),\n+        """\n \n         fig = go.Figure(data=[data], layout=layout)\n+        plotly.offline.plot(fig, filename="output.html",\n+                            auto_open=False)\n+        # to be discovered by `from_work_dir`\n+        os.rename(\'output.html\', \'output\')\n+\n+        return 0\n \n     elif plot_type == \'learning_curve\':\n         input_df = pd.read_csv(infile1, sep=\'\\t\', header=\'infer\')\n@@ -264,23 +495,57 @@\n             yaxis=dict(\n                 title=\'Performance Score\'\n             ),\n-            title=title or \'Learning Curve\'\n+            # modify these configurations to customize image\n+            title=dict(\n+                text=title or \'Learning Curve\',\n+                x=0.5,\n+                y=0.92,\n+                xanchor=\'center\',\n+                yanchor=\'top\'\n+            ),\n+            font=dict(\n+                family="sans-serif",\n+                size=11\n+            ),\n+            # control backgroud colors\n+            plot_bgcolor=\'rgba(255,255,255,0)\'\n         )\n+        """\n+        # legend=dict(\n+                # x=0.95,\n+                # y=0,\n+                # traceorder="normal",\n+                # font=dict(\n+                #    family="sans-serif",\n+                #    size=9,\n+                #    color="black"\n+                # ),\n+                # bgcolor="LightSteelBlue",\n+                # bordercolor="Black",\n+                # borderwidth=2\n+            # ),\n+        """\n+\n         fig = go.Figure(data=[data1, data2], layout=layout)\n+        plotly.offline.plot(fig, filename="output.html",\n+                            auto_open=False)\n+        # to be discovered by `from_work_dir`\n+        os.rename(\'output.html\', \'output\')\n+\n+        return 0\n \n     elif plot_type == \'keras_plot_model\':\n         with open(model_config, \'r\') as f:\n             model_str = f.read()\n         model = model_from_json(model_str)\n         plot_model(model, to_file="output.png")\n-        __import__(\'os\').rename(\'output.png\', \'output\')\n+        os.rename(\'output.png\', \'output\')\n \n         return 0\n \n-    plotly.offline.plot(fig, filename="output.html",\n-                        auto_open=False)\n-    # to be discovered by `from_work_dir`\n-    __import__(\'os\').rename(\'output.html\', \'output\')\n+    # save pdf file to disk\n+    # fig.write_image("image.pdf", format=\'pdf\')\n+    # fig.write_image("image.pdf", format=\'pdf\', width=340*2, height=226*2)\n \n \n if __name__ == \'__main__\':\n'
b
diff -r da0a22cdcce7 -r 2df8f5c30edc model_prediction.py
--- a/model_prediction.py Thu Nov 07 05:31:04 2019 -0500
+++ b/model_prediction.py Mon Dec 16 05:21:05 2019 -0500
[
@@ -2,13 +2,11 @@
 import json
 import numpy as np
 import pandas as pd
-import tabix
 import warnings
 
 from scipy.io import mmread
 from sklearn.pipeline import Pipeline
 
-from galaxy_ml.externals.selene_sdk.sequences import Genome
 from galaxy_ml.utils import (load_model, read_columns,
                              get_module, try_get_attr)
 
@@ -138,45 +136,10 @@
         pred_data_generator = klass(
             ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
 
-        pred_data_generator.fit()
+        pred_data_generator.set_processing_attrs()
 
         variants = pred_data_generator.variants
-        # TODO : remove the following block after galaxy-ml v0.7.13
-        blacklist_tabix = getattr(pred_data_generator.reference_genome_,
-                                  '_blacklist_tabix', None)
-        clean_variants = []
-        if blacklist_tabix:
-            start_radius = pred_data_generator.start_radius_
-            end_radius = pred_data_generator.end_radius_
 
-            for chrom, pos, name, ref, alt, strand in variants:
-                center = pos + len(ref) // 2
-                start = center - start_radius
-                end = center + end_radius
-
-                if isinstance(pred_data_generator.reference_genome_, Genome):
-                    if "chr" not in chrom:
-                        chrom = "chr" + chrom
-                    if "MT" in chrom:
-                        chrom = chrom[:-1]
-                try:
-                    rows = blacklist_tabix.query(chrom, start, end)
-                    found = 0
-                    for row in rows:
-                        found = 1
-                        break
-                    if found:
-                        continue
-                except tabix.TabixError:
-                    pass
-
-                clean_variants.append((chrom, pos, name, ref, alt, strand))
-        else:
-            clean_variants = variants
-
-        setattr(pred_data_generator, 'variants', clean_variants)
-
-        variants = np.array(clean_variants)
         # predict 1600 sample at once then write to file
         gen_flow = pred_data_generator.flow(batch_size=1600)
 
b
diff -r da0a22cdcce7 -r 2df8f5c30edc search_model_validation.py
--- a/search_model_validation.py Thu Nov 07 05:31:04 2019 -0500
+++ b/search_model_validation.py Mon Dec 16 05:21:05 2019 -0500
[
b'@@ -4,41 +4,35 @@\n import joblib\n import json\n import numpy as np\n+import os\n import pandas as pd\n import pickle\n import skrebate\n-import sklearn\n import sys\n-import xgboost\n import warnings\n-from imblearn import under_sampling, over_sampling, combine\n from scipy.io import mmread\n-from mlxtend import classifier, regressor\n-from sklearn.base import clone\n-from sklearn import (cluster, compose, decomposition, ensemble,\n-                     feature_extraction, feature_selection,\n-                     gaussian_process, kernel_approximation, metrics,\n-                     model_selection, naive_bayes, neighbors,\n-                     pipeline, preprocessing, svm, linear_model,\n-                     tree, discriminant_analysis)\n+from sklearn import (cluster, decomposition, feature_selection,\n+                     kernel_approximation, model_selection, preprocessing)\n from sklearn.exceptions import FitFailedWarning\n from sklearn.model_selection._validation import _score, cross_validate\n from sklearn.model_selection import _search, _validation\n+from sklearn.pipeline import Pipeline\n \n from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,\n-                             read_columns, try_get_attr, get_module)\n+                             read_columns, try_get_attr, get_module,\n+                             clean_params, get_main_estimator)\n \n \n _fit_and_score = try_get_attr(\'galaxy_ml.model_validations\', \'_fit_and_score\')\n setattr(_search, \'_fit_and_score\', _fit_and_score)\n setattr(_validation, \'_fit_and_score\', _fit_and_score)\n \n-N_JOBS = int(__import__(\'os\').environ.get(\'GALAXY_SLOTS\', 1))\n-CACHE_DIR = \'./cached\'\n+N_JOBS = int(os.environ.get(\'GALAXY_SLOTS\', 1))\n+# handle  disk cache\n+CACHE_DIR = os.path.join(os.getcwd(), \'cached\')\n+del os\n NON_SEARCHABLE = (\'n_jobs\', \'pre_dispatch\', \'memory\', \'_path\',\n                   \'nthread\', \'callbacks\')\n-ALLOWED_CALLBACKS = (\'EarlyStopping\', \'TerminateOnNaN\', \'ReduceLROnPlateau\',\n-                     \'CSVLogger\', \'None\')\n \n \n def _eval_search_params(params_builder):\n@@ -164,74 +158,40 @@\n     return search_params\n \n \n-def main(inputs, infile_estimator, infile1, infile2,\n-         outfile_result, outfile_object=None,\n-         outfile_weights=None, groups=None,\n-         ref_seq=None, intervals=None, targets=None,\n-         fasta_path=None):\n-    """\n-    Parameter\n-    ---------\n-    inputs : str\n-        File path to galaxy tool parameter\n+def _handle_X_y(estimator, params, infile1, infile2, loaded_df={},\n+                ref_seq=None, intervals=None, targets=None,\n+                fasta_path=None):\n+    """read inputs\n \n-    infile_estimator : str\n-        File path to estimator\n-\n+    Params\n+    -------\n+    estimator : estimator object\n+    params : dict\n+        Galaxy tool parameter inputs\n     infile1 : str\n         File path to dataset containing features\n-\n     infile2 : str\n         File path to dataset containing target values\n-\n-    outfile_result : str\n-        File path to save the results, either cv_results or test result\n-\n-    outfile_object : str, optional\n-        File path to save searchCV object\n-\n-    outfile_weights : str, optional\n-        File path to save model weights\n-\n-    groups : str\n-        File path to dataset containing groups labels\n-\n+    loaded_df : dict\n+        Contains loaded DataFrame objects with file path as keys\n     ref_seq : str\n         File path to dataset containing genome sequence file\n-\n-    intervals : str\n+    interval : str\n         File path to dataset containing interval file\n-\n     targets : str\n         File path to dataset compressed target bed file\n-\n     fasta_path : str\n         File path to dataset containing fasta file\n-    """\n-    warnings.simplefilter(\'ignore\')\n \n-    with open(inputs, \'r\') as param_handler:\n-        params = json.load(param_handler)\n-\n-    # conflict param checker\n-    if params[\'outer_split\'][\'split_mode\'] == \'nested_cv\' \\\n-            and params[\'save\'] != \'nope\':\n-        raise ValueEr'..b'split_options)\n-            else:\n-                if split_options[\'shuffle\'] == \'None\':\n-                    split_options[\'shuffle\'] = None\n-                X, X_test, y, y_test =\\\n-                    train_test_split(X, y, **split_options)\n-        # end train_test_split\n+        # deprecate train test split mode\n+        """searcher = _do_train_test_split_val(\n+            searcher, X, y, params,\n+            primary_scoring=primary_scoring,\n+            error_score=options[\'error_score\'],\n+            groups=groups,\n+            outfile=outfile_result)"""\n \n-        # shared by both train_test_split and non-split\n+    # no outer split\n+    else:\n+        searcher.set_params(n_jobs=N_JOBS)\n         if options[\'error_score\'] == \'raise\':\n             searcher.fit(X, y, groups=groups)\n         else:\n@@ -489,47 +646,14 @@\n                 for warning in w:\n                     print(repr(warning.message))\n \n-        # no outer split\n-        if split_mode == \'no\':\n-            # save results\n-            cv_results = pd.DataFrame(searcher.cv_results_)\n-            cv_results = cv_results[sorted(cv_results.columns)]\n-            cv_results.to_csv(path_or_buf=outfile_result, sep=\'\\t\',\n-                              header=True, index=False)\n-\n-        # train_test_split, output test result using best_estimator_\n-        # or rebuild the trained estimator using weights if applicable.\n-        else:\n-            scorer_ = searcher.scorer_\n-            if isinstance(scorer_, collections.Mapping):\n-                is_multimetric = True\n-            else:\n-                is_multimetric = False\n-\n-            best_estimator_ = getattr(searcher, \'best_estimator_\', None)\n-            if not best_estimator_:\n-                raise ValueError("GridSearchCV object has no "\n-                                 "`best_estimator_` when `refit`=False!")\n-\n-            if best_estimator_.__class__.__name__ == \'KerasGBatchClassifier\' \\\n-                    and hasattr(estimator.data_batch_generator, \'target_path\'):\n-                test_score = best_estimator_.evaluate(\n-                    X_test, scorer=scorer_, is_multimetric=is_multimetric)\n-            else:\n-                test_score = _score(best_estimator_, X_test,\n-                                    y_test, scorer_,\n-                                    is_multimetric=is_multimetric)\n-\n-            if not is_multimetric:\n-                test_score = {primary_scoring: test_score}\n-            for key, value in test_score.items():\n-                test_score[key] = [value]\n-            result_df = pd.DataFrame(test_score)\n-            result_df.to_csv(path_or_buf=outfile_result, sep=\'\\t\',\n-                             header=True, index=False)\n+        cv_results = pd.DataFrame(searcher.cv_results_)\n+        cv_results = cv_results[sorted(cv_results.columns)]\n+        cv_results.to_csv(path_or_buf=outfile_result, sep=\'\\t\',\n+                          header=True, index=False)\n \n     memory.clear(warn=False)\n \n+    # output best estimator, and weights if applicable\n     if outfile_object:\n         best_estimator_ = getattr(searcher, \'best_estimator_\', None)\n         if not best_estimator_:\n@@ -538,9 +662,10 @@\n                           "nested gridsearch or `refit` is False!")\n             return\n \n-        main_est = best_estimator_\n-        if isinstance(best_estimator_, pipeline.Pipeline):\n-            main_est = best_estimator_.steps[-1][-1]\n+        # clean prams\n+        best_estimator_ = clean_params(best_estimator_)\n+\n+        main_est = get_main_estimator(best_estimator_)\n \n         if hasattr(main_est, \'model_\') \\\n                 and hasattr(main_est, \'save_weights\'):\n@@ -554,6 +679,7 @@\n                 del main_est.data_generator_\n \n         with open(outfile_object, \'wb\') as output_handler:\n+            print("Best estimator is saved: %s " % repr(best_estimator_))\n             pickle.dump(best_estimator_, output_handler,\n                         pickle.HIGHEST_PROTOCOL)\n \n'
b
diff -r da0a22cdcce7 -r 2df8f5c30edc svm.xml
--- a/svm.xml Thu Nov 07 05:31:04 2019 -0500
+++ b/svm.xml Mon Dec 16 05:21:05 2019 -0500
[
@@ -31,7 +31,7 @@
 #if $selected_tasks.selected_task == "load":
 
 header = 'infer' if params["selected_tasks"]["header"] else None
-data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
+data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None)
 
 with open("$infile_model", 'rb') as model_handler:
     classifier_object = load_model(model_handler)
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/RandomForestClassifier.zip
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/StackingCVRegressor01.zip
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/StackingRegressor02.zip
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/StackingVoting03.zip
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/abc_model01
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/abr_model01
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/best_estimator_.zip
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/brier_score_loss.txt
--- a/test-data/brier_score_loss.txt Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/brier_score_loss.txt Mon Dec 16 05:21:05 2019 -0500
b
@@ -1,2 +1,2 @@
 brier_score_loss : 
-0.5641025641025641
+0.24051282051282052
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/classification_report.txt
--- a/test-data/classification_report.txt Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/classification_report.txt Mon Dec 16 05:21:05 2019 -0500
b
@@ -5,7 +5,7 @@
            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
+    accuracy                           0.85        39
    macro avg       0.87      0.88      0.84        39
 weighted avg       0.91      0.85      0.85        39
 
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/gbc_model01
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/gbr_model01
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/get_params05.tabular
--- a/test-data/get_params05.tabular Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/get_params05.tabular Mon Dec 16 05:21:05 2019 -0500
[
@@ -1,31 +1,18 @@
  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.
+@ bootstrap bootstrap: True
+@ criterion criterion: 'mse'
+@ max_depth max_depth: None
+@ max_features max_features: 'auto'
+@ max_leaf_nodes max_leaf_nodes: None
+@ min_impurity_decrease min_impurity_decrease: 0.0
+@ min_impurity_split min_impurity_split: None
+@ min_samples_leaf min_samples_leaf: 1
+@ min_samples_split min_samples_split: 2
+@ min_weight_fraction_leaf min_weight_fraction_leaf: 0.0
+@ n_estimators n_estimators: 100
+* n_jobs n_jobs: 1
+@ oob_score oob_score: False
+@ random_state random_state: 42
+* verbose verbose: 0
+@ warm_start warm_start: False
+ Note: @, params eligible for search in searchcv tool.
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/get_params12.tabular
--- a/test-data/get_params12.tabular Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/get_params12.tabular Mon Dec 16 05:21:05 2019 -0500
[
@@ -1,47 +1,32 @@
  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,
+@ estimator "estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
        colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
        max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
        n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
        reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
        silent=True, subsample=1)"
-@ 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.
+@ n_features_to_select n_features_to_select: None
+* step step: 1
+* verbose verbose: 0
+@ estimator__base_score estimator__base_score: 0.5
+@ estimator__booster estimator__booster: 'gbtree'
+@ estimator__colsample_bylevel estimator__colsample_bylevel: 1
+@ estimator__colsample_bytree estimator__colsample_bytree: 1
+@ estimator__gamma estimator__gamma: 0
+@ estimator__learning_rate estimator__learning_rate: 0.1
+@ estimator__max_delta_step estimator__max_delta_step: 0
+@ estimator__max_depth estimator__max_depth: 3
+@ estimator__min_child_weight estimator__min_child_weight: 1
+@ estimator__missing estimator__missing: nan
+@ estimator__n_estimators estimator__n_estimators: 100
+* estimator__n_jobs estimator__n_jobs: 1
+* estimator__nthread estimator__nthread: None
+@ estimator__objective estimator__objective: 'reg:linear'
+@ estimator__random_state estimator__random_state: 0
+@ estimator__reg_alpha estimator__reg_alpha: 0
+@ estimator__reg_lambda estimator__reg_lambda: 1
+@ estimator__scale_pos_weight estimator__scale_pos_weight: 1
+@ estimator__seed estimator__seed: None
+@ estimator__silent estimator__silent: True
+@ estimator__subsample estimator__subsample: 1
+ Note: @, params eligible for search in searchcv tool.
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/glm_model01
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/glm_model06
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Binary file test-data/glm_model06 has changed
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/glm_model07
b
Binary file test-data/glm_model07 has changed
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/glm_model08
b
Binary file test-data/glm_model08 has changed
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/glm_result01
--- a/test-data/glm_result01 Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/glm_result01 Mon Dec 16 05:21:05 2019 -0500
b
@@ -1,5 +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
+86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 20479602419382.055
+91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 21460309408632.004
+-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -11245419999724.842
+61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 14574106078789.26
+-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -48782519807586.32
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/glm_result02
--- a/test-data/glm_result02 Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/glm_result02 Mon Dec 16 05:21:05 2019 -0500
b
@@ -1,5 +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
+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
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/glm_result08
--- a/test-data/glm_result08 Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/glm_result08 Mon Dec 16 05:21:05 2019 -0500
b
@@ -1,4 +1,4 @@
-3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 0
+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
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras01.json
--- a/test-data/keras01.json Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/keras01.json Mon Dec 16 05:21:05 2019 -0500
[
@@ -1,1 +1,90 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 784], "dtype": "float32", "units": 32, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "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
+{
+  "class_name": "Sequential",
+  "config": {
+    "name": "sequential_1",
+    "layers": [
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_1",
+          "trainable": true,
+          "batch_input_shape": [
+            null,
+            784
+          ],
+          "dtype": "float32",
+          "units": 32,
+          "activation": "linear",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        }
+      },
+      {
+        "class_name": "Activation",
+        "config": {
+          "name": "activation_1",
+          "trainable": true,
+          "dtype": "float32",
+          "activation": "relu"
+        }
+      },
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_2",
+          "trainable": true,
+          "dtype": "float32",
+          "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,
+          "dtype": "float32",
+          "activation": "softmax"
+        }
+      }
+    ]
+  },
+  "keras_version": "2.3.1",
+  "backend": "tensorflow"
+}
\ No newline at end of file
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras02.json
--- a/test-data/keras02.json Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/keras02.json Mon Dec 16 05:21:05 2019 -0500
[
b'@@ -1,1 +1,385 @@\n-{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "main_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 100], "dtype": "int32", "sparse": false, "name": "main_input"}, "inbound_nodes": []}, {"name": "embedding_1", "class_name": "Embedding", "config": {"name": "embedding_1", "trainable": true, "batch_input_shape": [null, 100], "dtype": "float32", "input_dim": 10000, "output_dim": 512, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -0.05, "maxval": 0.05, "seed": null}}, "embeddings_regularizer": null, "activity_regularizer": null, "embeddings_constraint": null, "mask_zero": false, "input_length": 100}, "inbound_nodes": [[["main_input", 0, 0, {}]]]}, {"name": "lstm_1", "class_name": "LSTM", "config": {"name": "lstm_1", "trainable": true, "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 32, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 1}, "inbound_nodes": [[["embedding_1", 0, 0, {}]]]}, {"name": "dense_1", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["lstm_1", 0, 0, {}]]]}, {"name": "aux_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 5], "dtype": "float32", "sparse": false, "name": "aux_input"}, "inbound_nodes": []}, {"name": "concatenate_1", "class_name": "Concatenate", "config": {"name": "concatenate_1", "trainable": true, "axis": -1}, "inbound_nodes": [[["dense_1", 0, 0, {}], ["aux_input", 0, 0, {}]]]}, {"name": "dense_2", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_1", 0, 0, {}]]]}, {"name": "dense_3", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_2", 0, 0, {}]]]}, {"name": "dense_4", "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_re'..b'"bias_initializer": {\n+            "class_name": "Zeros",\n+            "config": {}\n+          },\n+          "kernel_regularizer": null,\n+          "bias_regularizer": null,\n+          "activity_regularizer": null,\n+          "kernel_constraint": null,\n+          "bias_constraint": null\n+        },\n+        "inbound_nodes": [\n+          [\n+            [\n+              "concatenate_1",\n+              0,\n+              0,\n+              {}\n+            ]\n+          ]\n+        ]\n+      },\n+      {\n+        "name": "dense_3",\n+        "class_name": "Dense",\n+        "config": {\n+          "name": "dense_3",\n+          "trainable": true,\n+          "dtype": "float32",\n+          "units": 64,\n+          "activation": "relu",\n+          "use_bias": true,\n+          "kernel_initializer": {\n+            "class_name": "VarianceScaling",\n+            "config": {\n+              "scale": 1.0,\n+              "mode": "fan_avg",\n+              "distribution": "uniform",\n+              "seed": null\n+            }\n+          },\n+          "bias_initializer": {\n+            "class_name": "Zeros",\n+            "config": {}\n+          },\n+          "kernel_regularizer": null,\n+          "bias_regularizer": null,\n+          "activity_regularizer": null,\n+          "kernel_constraint": null,\n+          "bias_constraint": null\n+        },\n+        "inbound_nodes": [\n+          [\n+            [\n+              "dense_2",\n+              0,\n+              0,\n+              {}\n+            ]\n+          ]\n+        ]\n+      },\n+      {\n+        "name": "dense_4",\n+        "class_name": "Dense",\n+        "config": {\n+          "name": "dense_4",\n+          "trainable": true,\n+          "dtype": "float32",\n+          "units": 64,\n+          "activation": "relu",\n+          "use_bias": true,\n+          "kernel_initializer": {\n+            "class_name": "VarianceScaling",\n+            "config": {\n+              "scale": 1.0,\n+              "mode": "fan_avg",\n+              "distribution": "uniform",\n+              "seed": null\n+            }\n+          },\n+          "bias_initializer": {\n+            "class_name": "Zeros",\n+            "config": {}\n+          },\n+          "kernel_regularizer": null,\n+          "bias_regularizer": null,\n+          "activity_regularizer": null,\n+          "kernel_constraint": null,\n+          "bias_constraint": null\n+        },\n+        "inbound_nodes": [\n+          [\n+            [\n+              "dense_3",\n+              0,\n+              0,\n+              {}\n+            ]\n+          ]\n+        ]\n+      },\n+      {\n+        "name": "dense_5",\n+        "class_name": "Dense",\n+        "config": {\n+          "name": "dense_5",\n+          "trainable": true,\n+          "dtype": "float32",\n+          "units": 1,\n+          "activation": "sigmoid",\n+          "use_bias": true,\n+          "kernel_initializer": {\n+            "class_name": "VarianceScaling",\n+            "config": {\n+              "scale": 1.0,\n+              "mode": "fan_avg",\n+              "distribution": "uniform",\n+              "seed": null\n+            }\n+          },\n+          "bias_initializer": {\n+            "class_name": "Zeros",\n+            "config": {}\n+          },\n+          "kernel_regularizer": null,\n+          "bias_regularizer": null,\n+          "activity_regularizer": null,\n+          "kernel_constraint": null,\n+          "bias_constraint": null\n+        },\n+        "inbound_nodes": [\n+          [\n+            [\n+              "dense_4",\n+              0,\n+              0,\n+              {}\n+            ]\n+          ]\n+        ]\n+      }\n+    ],\n+    "input_layers": [\n+      [\n+        "main_input",\n+        0,\n+        0\n+      ],\n+      [\n+        "aux_input",\n+        0,\n+        0\n+      ]\n+    ],\n+    "output_layers": [\n+      [\n+        "dense_1",\n+        0,\n+        0\n+      ],\n+      [\n+        "dense_5",\n+        0,\n+        0\n+      ]\n+    ]\n+  },\n+  "keras_version": "2.3.1",\n+  "backend": "tensorflow"\n+}\n\\ No newline at end of file\n'
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras04.json
--- a/test-data/keras04.json Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/keras04.json Mon Dec 16 05:21:05 2019 -0500
[
@@ -1,1 +1,90 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 17], "dtype": "float32", "units": 32, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "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
+{
+  "class_name": "Sequential",
+  "config": {
+    "name": "sequential_1",
+    "layers": [
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_1",
+          "trainable": true,
+          "batch_input_shape": [
+            null,
+            17
+          ],
+          "dtype": "float32",
+          "units": 32,
+          "activation": "linear",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        }
+      },
+      {
+        "class_name": "Activation",
+        "config": {
+          "name": "activation_1",
+          "trainable": true,
+          "dtype": "float32",
+          "activation": "linear"
+        }
+      },
+      {
+        "class_name": "Dense",
+        "config": {
+          "name": "dense_2",
+          "trainable": true,
+          "dtype": "float32",
+          "units": 1,
+          "activation": "linear",
+          "use_bias": true,
+          "kernel_initializer": {
+            "class_name": "VarianceScaling",
+            "config": {
+              "scale": 1.0,
+              "mode": "fan_avg",
+              "distribution": "uniform",
+              "seed": null
+            }
+          },
+          "bias_initializer": {
+            "class_name": "Zeros",
+            "config": {}
+          },
+          "kernel_regularizer": null,
+          "bias_regularizer": null,
+          "activity_regularizer": null,
+          "kernel_constraint": null,
+          "bias_constraint": null
+        }
+      },
+      {
+        "class_name": "Activation",
+        "config": {
+          "name": "activation_2",
+          "trainable": true,
+          "dtype": "float32",
+          "activation": "linear"
+        }
+      }
+    ]
+  },
+  "keras_version": "2.3.1",
+  "backend": "tensorflow"
+}
\ No newline at end of file
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_batch_model01
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Binary file test-data/keras_batch_model01 has changed
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_batch_model02
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Binary file test-data/keras_batch_model02 has changed
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_batch_model04
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Binary file test-data/keras_batch_model04 has changed
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_batch_params01.tabular
--- a/test-data/keras_batch_params01.tabular Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/keras_batch_params01.tabular Mon Dec 16 05:21:05 2019 -0500
[
@@ -6,15 +6,14 @@
 @ 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)"
+@ 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
+@ layers_1_Activation layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32
+@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units'
+@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32
 @ loss loss: 'binary_crossentropy'
 @ lr lr: 0.01
 @ metrics metrics: ['acc']
@@ -60,12 +59,13 @@
 @ 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 layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}
 @ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'relu'
+@ layers_1_Activation__config__dtype layers_1_Activation__config__dtype: 'float32'
 * layers_1_Activation__config__name layers_1_Activation__config__name: 'activation_1'
 @ layers_1_Activation__config__trainable layers_1_Activation__config__trainable: True
 * layers_2_Dense__class_name layers_2_Dense__class_name: 'Dense'
-@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'units': 10, 'activation': 'linear', 'use_bias': True, 'kerne
+@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'linear', 'use
 @ layers_2_Dense__config__activation layers_2_Dense__config__activation: 'linear'
 @ layers_2_Dense__config__activity_regularizer layers_2_Dense__config__activity_regularizer: None
 @ layers_2_Dense__config__bias_constraint layers_2_Dense__config__bias_constraint: None
@@ -73,6 +73,7 @@
 * layers_2_Dense__config__bias_initializer__class_name layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
 @ layers_2_Dense__config__bias_initializer__config layers_2_Dense__config__bias_initializer__config: {}
 @ layers_2_Dense__config__bias_regularizer layers_2_Dense__config__bias_regularizer: None
+@ layers_2_Dense__config__dtype layers_2_Dense__config__dtype: 'float32'
 @ layers_2_Dense__config__kernel_constraint layers_2_Dense__config__kernel_constraint: None
 @ layers_2_Dense__config__kernel_initializer layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
 * layers_2_Dense__config__kernel_initializer__class_name layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
@@ -87,8 +88,9 @@
 @ 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 layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'softmax'}
 @ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'softmax'
+@ layers_3_Activation__config__dtype layers_3_Activation__config__dtype: 'float32'
 * layers_3_Activation__config__name layers_3_Activation__config__name: 'activation_2'
 @ layers_3_Activation__config__trainable layers_3_Activation__config__trainable: True
  Note: @, params eligible for search in searchcv tool.
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_batch_params04.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras_batch_params04.tabular Mon Dec 16 05:21:05 2019 -0500
[
@@ -0,0 +1,91 @@
+ Parameter Value
+@ amsgrad amsgrad: None
+@ batch_size batch_size: 32
+@ beta_1 beta_1: None
+@ beta_2 beta_2: None
+@ callbacks callbacks: [{'callback_selection': {'callback_type': 'None'}}]
+@ class_positive_factor class_positive_factor: 1.0
+@ config config: {'name': 'sequential_1', 'layers': [{'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable
+@ data_batch_generator data_batch_generator: None
+@ decay decay: 0.0
+@ epochs epochs: 100
+@ layers_0_Dense layers_0_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'batch_input_shape': [None,
+@ layers_1_Activation layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32
+@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units'
+@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32
+@ loss loss: 'binary_crossentropy'
+@ lr lr: 0.01
+@ metrics metrics: ['acc']
+@ model_type model_type: 'sequential'
+@ momentum momentum: 0.0
+* n_jobs n_jobs: 1
+@ nesterov nesterov: False
+@ optimizer optimizer: 'sgd'
+@ prediction_steps prediction_steps: None
+@ rho rho: None
+@ schedule_decay schedule_decay: None
+@ seed seed: None
+@ steps_per_epoch steps_per_epoch: None
+@ validation_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, 784], 'dtype': 'float32', 'units'
+@ layers_0_Dense__config__activation layers_0_Dense__config__activation: 'linear'
+@ layers_0_Dense__config__activity_regularizer layers_0_Dense__config__activity_regularizer: None
+@ layers_0_Dense__config__batch_input_shape layers_0_Dense__config__batch_input_shape: [None, 784]
+@ layers_0_Dense__config__bias_constraint layers_0_Dense__config__bias_constraint: None
+@ layers_0_Dense__config__bias_initializer layers_0_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+* layers_0_Dense__config__bias_initializer__class_name layers_0_Dense__config__bias_initializer__class_name: 'Zeros'
+@ layers_0_Dense__config__bias_initializer__config layers_0_Dense__config__bias_initializer__config: {}
+@ layers_0_Dense__config__bias_regularizer layers_0_Dense__config__bias_regularizer: None
+@ layers_0_Dense__config__dtype layers_0_Dense__config__dtype: 'float32'
+@ layers_0_Dense__config__kernel_constraint layers_0_Dense__config__kernel_constraint: None
+@ layers_0_Dense__config__kernel_initializer layers_0_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+* layers_0_Dense__config__kernel_initializer__class_name layers_0_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@ layers_0_Dense__config__kernel_initializer__config layers_0_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@ layers_0_Dense__config__kernel_initializer__config__distribution layers_0_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@ layers_0_Dense__config__kernel_initializer__config__mode layers_0_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@ layers_0_Dense__config__kernel_initializer__config__scale layers_0_Dense__config__kernel_initializer__config__scale: 1.0
+@ layers_0_Dense__config__kernel_initializer__config__seed layers_0_Dense__config__kernel_initializer__config__seed: None
+@ layers_0_Dense__config__kernel_regularizer layers_0_Dense__config__kernel_regularizer: None
+* layers_0_Dense__config__name layers_0_Dense__config__name: 'dense_1'
+@ layers_0_Dense__config__trainable layers_0_Dense__config__trainable: True
+@ layers_0_Dense__config__units layers_0_Dense__config__units: 32
+@ layers_0_Dense__config__use_bias layers_0_Dense__config__use_bias: True
+* layers_1_Activation__class_name layers_1_Activation__class_name: 'Activation'
+@ layers_1_Activation__config layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}
+@ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'relu'
+@ layers_1_Activation__config__dtype layers_1_Activation__config__dtype: 'float32'
+* layers_1_Activation__config__name layers_1_Activation__config__name: 'activation_1'
+@ layers_1_Activation__config__trainable layers_1_Activation__config__trainable: True
+* layers_2_Dense__class_name layers_2_Dense__class_name: 'Dense'
+@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'linear', 'use
+@ layers_2_Dense__config__activation layers_2_Dense__config__activation: 'linear'
+@ layers_2_Dense__config__activity_regularizer layers_2_Dense__config__activity_regularizer: None
+@ layers_2_Dense__config__bias_constraint layers_2_Dense__config__bias_constraint: None
+@ layers_2_Dense__config__bias_initializer layers_2_Dense__config__bias_initializer: {'class_name': 'Zeros', 'config': {}}
+* layers_2_Dense__config__bias_initializer__class_name layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
+@ layers_2_Dense__config__bias_initializer__config layers_2_Dense__config__bias_initializer__config: {}
+@ layers_2_Dense__config__bias_regularizer layers_2_Dense__config__bias_regularizer: None
+@ layers_2_Dense__config__dtype layers_2_Dense__config__dtype: 'float32'
+@ layers_2_Dense__config__kernel_constraint layers_2_Dense__config__kernel_constraint: None
+@ layers_2_Dense__config__kernel_initializer layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
+* layers_2_Dense__config__kernel_initializer__class_name layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
+@ layers_2_Dense__config__kernel_initializer__config layers_2_Dense__config__kernel_initializer__config: {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}
+@ layers_2_Dense__config__kernel_initializer__config__distribution layers_2_Dense__config__kernel_initializer__config__distribution: 'uniform'
+@ layers_2_Dense__config__kernel_initializer__config__mode layers_2_Dense__config__kernel_initializer__config__mode: 'fan_avg'
+@ layers_2_Dense__config__kernel_initializer__config__scale layers_2_Dense__config__kernel_initializer__config__scale: 1.0
+@ layers_2_Dense__config__kernel_initializer__config__seed layers_2_Dense__config__kernel_initializer__config__seed: None
+@ layers_2_Dense__config__kernel_regularizer layers_2_Dense__config__kernel_regularizer: None
+* layers_2_Dense__config__name layers_2_Dense__config__name: 'dense_2'
+@ layers_2_Dense__config__trainable layers_2_Dense__config__trainable: True
+@ layers_2_Dense__config__units layers_2_Dense__config__units: 10
+@ layers_2_Dense__config__use_bias layers_2_Dense__config__use_bias: True
+* layers_3_Activation__class_name layers_3_Activation__class_name: 'Activation'
+@ layers_3_Activation__config layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'softmax'}
+@ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'softmax'
+@ layers_3_Activation__config__dtype layers_3_Activation__config__dtype: 'float32'
+* layers_3_Activation__config__name layers_3_Activation__config__name: 'activation_2'
+@ layers_3_Activation__config__trainable layers_3_Activation__config__trainable: True
+ Note: @, params eligible for search in searchcv tool.
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_model01
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Binary file test-data/keras_model01 has changed
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_model02
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Binary file test-data/keras_model02 has changed
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_model04
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Binary file test-data/keras_model04 has changed
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_params04.tabular
--- a/test-data/keras_params04.tabular Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/keras_params04.tabular Mon Dec 16 05:21:05 2019 -0500
[
@@ -7,11 +7,10 @@
 @ 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
+@ layers_1_Activation layers_1_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_1', 'trainable': True, 'dtype': 'float32
+@ layers_2_Dense layers_2_Dense: {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units'
+@ layers_3_Activation layers_3_Activation: {'class_name': 'Activation', 'config': {'name': 'activation_2', 'trainable': True, 'dtype': 'float32
 @ loss loss: 'mean_squared_error'
 @ lr lr: 0.001
 @ metrics metrics: ['mse']
@@ -51,12 +50,13 @@
 @ 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 layers_1_Activation__config: {'name': 'activation_1', 'trainable': True, 'dtype': 'float32', 'activation': 'linear'}
 @ layers_1_Activation__config__activation layers_1_Activation__config__activation: 'linear'
+@ layers_1_Activation__config__dtype layers_1_Activation__config__dtype: 'float32'
 * layers_1_Activation__config__name layers_1_Activation__config__name: 'activation_1'
 @ layers_1_Activation__config__trainable layers_1_Activation__config__trainable: True
 * layers_2_Dense__class_name layers_2_Dense__class_name: 'Dense'
-@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'units': 1, 'activation': 'linear', 'use_bias': True, 'kernel
+@ layers_2_Dense__config layers_2_Dense__config: {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 1, 'activation': 'linear', 'use_
 @ layers_2_Dense__config__activation layers_2_Dense__config__activation: 'linear'
 @ layers_2_Dense__config__activity_regularizer layers_2_Dense__config__activity_regularizer: None
 @ layers_2_Dense__config__bias_constraint layers_2_Dense__config__bias_constraint: None
@@ -64,6 +64,7 @@
 * layers_2_Dense__config__bias_initializer__class_name layers_2_Dense__config__bias_initializer__class_name: 'Zeros'
 @ layers_2_Dense__config__bias_initializer__config layers_2_Dense__config__bias_initializer__config: {}
 @ layers_2_Dense__config__bias_regularizer layers_2_Dense__config__bias_regularizer: None
+@ layers_2_Dense__config__dtype layers_2_Dense__config__dtype: 'float32'
 @ layers_2_Dense__config__kernel_constraint layers_2_Dense__config__kernel_constraint: None
 @ layers_2_Dense__config__kernel_initializer layers_2_Dense__config__kernel_initializer: {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'unifo
 * layers_2_Dense__config__kernel_initializer__class_name layers_2_Dense__config__kernel_initializer__class_name: 'VarianceScaling'
@@ -78,8 +79,9 @@
 @ 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 layers_3_Activation__config: {'name': 'activation_2', 'trainable': True, 'dtype': 'float32', 'activation': 'linear'}
 @ layers_3_Activation__config__activation layers_3_Activation__config__activation: 'linear'
+@ layers_3_Activation__config__dtype layers_3_Activation__config__dtype: 'float32'
 * layers_3_Activation__config__name layers_3_Activation__config__name: 'activation_2'
 @ layers_3_Activation__config__trainable layers_3_Activation__config__trainable: True
  Note: @, params eligible for search in searchcv tool.
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_prefitted01.zip
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_save_weights01.h5
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/keras_train_eval_y_true02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/lda_model01
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/lda_model02
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/ml_vis01.html
--- a/test-data/ml_vis01.html Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/ml_vis01.html Mon Dec 16 05:21:05 2019 -0500
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/ml_vis02.html
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/ml_vis03.html
--- a/test-data/ml_vis03.html Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/ml_vis03.html Mon Dec 16 05:21:05 2019 -0500
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b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/ml_vis04.html
--- a/test-data/ml_vis04.html Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/ml_vis04.html Mon Dec 16 05:21:05 2019 -0500
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b'@@ -1,14 +1,31 @@\n-<html><head><meta charset="utf-8" /></head><body><script type="text/javascript">/**\n-* plotly.js v1.39.4\n-* Copyright 2012-2018, Plotly, Inc.\n+<html>\n+<head><meta charset="utf-8" /></head>\n+<body>\n+    <div>\n+        \n+                <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: \'local\'};</script>\n+        <script type="text/javascript">/**\n+* plotly.js v1.51.1\n+* Copyright 2012-2019, Plotly, Inc.\n * All rights reserved.\n * Licensed under the MIT license\n */\n-!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module \'"+o+"\'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,function(t){var r=e[o][1][t];return i(r||t)},u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}}()({1:[function(t,e,r){"use strict";var n=t("../src/lib"),i={"X,X div":"direction:ltr;font-family:\'Open Sans\', verdana, arial, sans-serif;margin:0;padding:0;","X input,X button":"font-family:\'Open Sans\', verdana, arial, sans-serif;","X input:focus,X button:focus":"outline:none;","X a":"text-decoration:none;","X a:hover":"text-decoration:none;","X .crisp":"shape-rendering:crispEdges;","X .user-select-none":"-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;user-select:none;","X svg":"overflow:hidden;","X svg a":"fill:#447adb;","X svg a:hover":"fill:#3c6dc5;","X .main-svg":"position:absolute;top:0;left:0;pointer-events:none;","X .main-svg .draglayer":"pointer-events:all;","X .cursor-default":"cursor:default;","X .cursor-pointer":"cursor:pointer;","X .cursor-crosshair":"cursor:crosshair;","X .cursor-move":"cursor:move;","X .cursor-col-resize":"cursor:col-resize;","X .cursor-row-resize":"cursor:row-resize;","X .cursor-ns-resize":"cursor:ns-resize;","X .cursor-ew-resize":"cursor:ew-resize;","X .cursor-sw-resize":"cursor:sw-resize;","X .cursor-s-resize":"cursor:s-resize;","X .cursor-se-resize":"cursor:se-resize;","X .cursor-w-resize":"cursor:w-resize;","X .cursor-e-resize":"cursor:e-resize;","X .cursor-nw-resize":"cursor:nw-resize;","X .cursor-n-resize":"cursor:n-resize;","X .cursor-ne-resize":"cursor:ne-resize;","X .cursor-grab":"cursor:-webkit-grab;cursor:grab;","X .modebar":"position:absolute;top:2px;right:2px;z-index:1001;background:rgba(255,255,255,0.7);","X .modebar--hover":"opacity:0;-webkit-transition:opacity 0.3s ease 0s;-moz-transition:opacity 0.3s ease 0s;-ms-transition:opacity 0.3s ease 0s;-o-transition:opacity 0.3s ease 0s;transition:opacity 0.3s ease 0s;","X:hover .modebar--hover":"opacity:1;","X .modebar-group":"float:left;display:inline-block;box-sizing:border-box;margin-left:8px;position:relative;vertical-align:middle;white-space:nowrap;","X .modebar-group:first-child":"margin-left:0px;","X .modebar-btn":"position:relative;font-size:16px;padding:3px 4px;cursor:pointer;line-height:normal;box-sizing:border-box;","X .modebar-btn svg":"position:relative;top:2px;","X .modebar-btn path":"fill:rgba(0,31,95,0.3);","X .modebar-btn.active path,X .modebar-btn:hover path":"fill:rgba(0,22,72,0.5);","X .modebar-btn.modebar-btn--logo":"padding:3px 1px;","X .modebar-btn.modebar-btn--logo path":"fill:#447adb !important;","X [data-title]:before,X [data-title]:after":"position:absolute;-webkit-transform:translate3d(0, 0, 0);-moz-transform:translate3d(0, 0, 0);-ms-transform:translate3d(0, 0, 0);-o-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0);display:none;opacity:0;z-index:1001;pointer-events:none;top:110%;right:50%;","X [data-title]:hover:before,X [data-ti'..b'"ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0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b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/ml_vis05.html
--- a/test-data/ml_vis05.html Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/ml_vis05.html Mon Dec 16 05:21:05 2019 -0500
[
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/named_steps.txt
--- a/test-data/named_steps.txt Thu Nov 07 05:31:04 2019 -0500
+++ b/test-data/named_steps.txt Mon Dec 16 05:21:05 2019 -0500
b
@@ -1,6 +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)}
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+{'preprocessing_1': SelectKBest(k=10, score_func=<function f_regression at 0x11b4ba8c8>), '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)}
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diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/pipeline_params05.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pipeline_params05.tabular Mon Dec 16 05:21:05 2019 -0500
b
@@ -0,0 +1,18 @@
+ Parameter Value
+@ bootstrap bootstrap: True
+@ criterion criterion: 'mse'
+@ max_depth max_depth: None
+@ max_features max_features: 'auto'
+@ max_leaf_nodes max_leaf_nodes: None
+@ min_impurity_decrease min_impurity_decrease: 0.0
+@ min_impurity_split min_impurity_split: None
+@ min_samples_leaf min_samples_leaf: 1
+@ min_samples_split min_samples_split: 2
+@ min_weight_fraction_leaf min_weight_fraction_leaf: 0.0
+@ n_estimators n_estimators: 100
+* n_jobs n_jobs: 1
+@ oob_score oob_score: False
+@ random_state random_state: 42
+* verbose verbose: 0
+@ warm_start warm_start: False
+ Note: @, params eligible for search in searchcv tool.
b
diff -r da0a22cdcce7 -r 2df8f5c30edc test-data/pipeline_params18
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pipeline_params18 Mon Dec 16 05:21:05 2019 -0500
[
@@ -0,0 +1,89 @@
+ Parameter Value
+* memory memory: None
+@ powertransformer powertransformer: PowerTransformer(copy=True, method='yeo-johnson', standardize=True)
+* steps "steps: [('powertransformer', PowerTransformer(copy=True, method='yeo-johnson', standardize=True)), ('transformedtargetregressor', TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None,
+                           regressor=RandomForestRegressor(bootstrap=True,
+                                                           criterion='mse',
+                                                           max_depth=None,
+                                                           max_features='auto',
+                                                           max_leaf_nodes=None,
+                                                           min_impurity_decrease=0.0,
+                                                           min_impurity_split=None,
+                                                           min_samples_leaf=1,
+                                                           min_samples_split=2,
+                                                           min_weight_fraction_leaf=0.0,
+                                                           n_estimators='warn',
+                                                           n_jobs=1,
+                                                           oob_score=False,
+                                                           random_state=10,
+                                                           verbose=0,
+                                                           warm_start=False),
+                           transformer=QuantileTransformer(copy=True,
+                                                           ignore_implicit_zeros=False,
+                                                           n_quantiles=1000,
+                                                           output_distribution='uniform',
+                                                           random_state=10,
+                                                           subsample=100000)))]"
+@ transformedtargetregressor "transformedtargetregressor: TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None,
+                           regressor=RandomForestRegressor(bootstrap=True,
+                                                           criterion='mse',
+                                                           max_depth=None,
+                                                           max_features='auto',
+                                                           max_leaf_nodes=None,
+                                                           min_impurity_decrease=0.0,
+                                                           min_impurity_split=None,
+                                                           min_samples_leaf=1,
+                                                           min_samples_split=2,
+                                                           min_weight_fraction_leaf=0.0,
+                                                           n_estimators='warn',
+                                                           n_jobs=1,
+                                                           oob_score=False,
+                                                           random_state=10,
+                                                           verbose=0,
+                                                           warm_start=False),
+                           transformer=QuantileTransformer(copy=True,
+                                                           ignore_implicit_zeros=False,
+                                                           n_quantiles=1000,
+                                                           output_distribution='uniform',
+                                                           random_state=10,
+                                                           subsample=100000))"
+* verbose verbose: False
+@ powertransformer__copy powertransformer__copy: True
+@ powertransformer__method powertransformer__method: 'yeo-johnson'
+@ powertransformer__standardize powertransformer__standardize: True
+@ transformedtargetregressor__check_inverse transformedtargetregressor__check_inverse: True
+@ transformedtargetregressor__func transformedtargetregressor__func: None
+@ transformedtargetregressor__inverse_func transformedtargetregressor__inverse_func: None
+@ transformedtargetregressor__regressor "transformedtargetregressor__regressor: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
+                      max_features='auto', max_leaf_nodes=None,
+                      min_impurity_decrease=0.0, min_impurity_split=None,
+                      min_samples_leaf=1, min_samples_split=2,
+                      min_weight_fraction_leaf=0.0, n_estimators='warn',
+                      n_jobs=1, oob_score=False, random_state=10, verbose=0,
+                      warm_start=False)"
+@ transformedtargetregressor__regressor__bootstrap transformedtargetregressor__regressor__bootstrap: True
+@ transformedtargetregressor__regressor__criterion transformedtargetregressor__regressor__criterion: 'mse'
+@ transformedtargetregressor__regressor__max_depth transformedtargetregressor__regressor__max_depth: None
+@ transformedtargetregressor__regressor__max_features transformedtargetregressor__regressor__max_features: 'auto'
+@ transformedtargetregressor__regressor__max_leaf_nodes transformedtargetregressor__regressor__max_leaf_nodes: None
+@ transformedtargetregressor__regressor__min_impurity_decrease transformedtargetregressor__regressor__min_impurity_decrease: 0.0
+@ transformedtargetregressor__regressor__min_impurity_split transformedtargetregressor__regressor__min_impurity_split: None
+@ transformedtargetregressor__regressor__min_samples_leaf transformedtargetregressor__regressor__min_samples_leaf: 1
+@ transformedtargetregressor__regressor__min_samples_split transformedtargetregressor__regressor__min_samples_split: 2
+@ transformedtargetregressor__regressor__min_weight_fraction_leaf transformedtargetregressor__regressor__min_weight_fraction_leaf: 0.0
+@ transformedtargetregressor__regressor__n_estimators transformedtargetregressor__regressor__n_estimators: 'warn'
+* transformedtargetregressor__regressor__n_jobs transformedtargetregressor__regressor__n_jobs: 1
+@ transformedtargetregressor__regressor__oob_score transformedtargetregressor__regressor__oob_score: False
+@ transformedtargetregressor__regressor__random_state transformedtargetregressor__regressor__random_state: 10
+* transformedtargetregressor__regressor__verbose transformedtargetregressor__regressor__verbose: 0
+@ transformedtargetregressor__regressor__warm_start transformedtargetregressor__regressor__warm_start: False
+@ transformedtargetregressor__transformer "transformedtargetregressor__transformer: QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000,
+                    output_distribution='uniform', random_state=10,
+                    subsample=100000)"
+@ transformedtargetregressor__transformer__copy transformedtargetregressor__transformer__copy: True
+@ transformedtargetregressor__transformer__ignore_implicit_zeros transformedtargetregressor__transformer__ignore_implicit_zeros: False
+@ transformedtargetregressor__transformer__n_quantiles transformedtargetregressor__transformer__n_quantiles: 1000
+@ transformedtargetregressor__transformer__output_distribution transformedtargetregressor__transformer__output_distribution: 'uniform'
+@ transformedtargetregressor__transformer__random_state transformedtargetregressor__transformer__random_state: 10
+@ transformedtargetregressor__transformer__subsample transformedtargetregressor__transformer__subsample: 100000
+ Note: @, params eligible for search in searchcv tool.
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diff -r da0a22cdcce7 -r 2df8f5c30edc train_test_eval.py
--- a/train_test_eval.py Thu Nov 07 05:31:04 2019 -0500
+++ b/train_test_eval.py Mon Dec 16 05:21:05 2019 -0500
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@@ -2,6 +2,7 @@
 import joblib
 import json
 import numpy as np
+import os
 import pandas as pd
 import pickle
 import warnings
@@ -29,8 +30,9 @@
 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'
+N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
+CACHE_DIR = os.path.join(os.getcwd(), 'cached')
+del os
 NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
                   'nthread', 'callbacks')
 ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',