| Previous changeset 1:da9f0b8ca61c (2019-11-07) Next changeset 3:4a1cd790e323 (2020-01-22) |
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Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476" |
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modified:
keras_deep_learning.py keras_macros.xml main_macros.xml ml_visualization_ex.py model_prediction.py search_model_validation.py 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 |
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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 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 keras_deep_learning.py --- a/keras_deep_learning.py Thu Nov 07 05:27:35 2019 -0500 +++ b/keras_deep_learning.py Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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, |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 keras_macros.xml --- a/keras_macros.xml Thu Nov 07 05:27:35 2019 -0500 +++ b/keras_macros.xml Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 < beta < 1. Generally close to 1."/> <param argument="beta_2" type="float" value="0.999" optional="true" label="beta_2" help="float, 0 < beta < 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 < beta < 1."/> </expand> </when> |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 keras_train_and_eval.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/keras_train_and_eval.py Mon Dec 16 05:18:07 2019 -0500 |
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| 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' |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 main_macros.xml --- a/main_macros.xml Thu Nov 07 05:27:35 2019 -0500 +++ b/main_macros.xml Mon Dec 16 05:18:07 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="'"/>\n- <add value="""/>\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' |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 ml_visualization_ex.py --- a/ml_visualization_ex.py Thu Nov 07 05:27:35 2019 -0500 +++ b/ml_visualization_ex.py Mon Dec 16 05:18:07 2019 -0500 |
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| 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' |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 model_prediction.py --- a/model_prediction.py Thu Nov 07 05:27:35 2019 -0500 +++ b/model_prediction.py Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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) |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 search_model_validation.py --- a/search_model_validation.py Thu Nov 07 05:27:35 2019 -0500 +++ b/search_model_validation.py Mon Dec 16 05:18:07 2019 -0500 |
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| 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' |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/RandomForestClassifier.zip |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/abc_model01 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/brier_score_loss.txt --- a/test-data/brier_score_loss.txt Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/brier_score_loss.txt Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -1,2 +1,2 @@ brier_score_loss : -0.5641025641025641 +0.24051282051282052 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/classification_report.txt --- a/test-data/classification_report.txt Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/classification_report.txt Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 da9f0b8ca61c -r cf54bae8ad42 test-data/get_params05.tabular --- a/test-data/get_params05.tabular Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/get_params05.tabular Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 da9f0b8ca61c -r cf54bae8ad42 test-data/get_params12.tabular --- a/test-data/get_params12.tabular Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/get_params12.tabular Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 da9f0b8ca61c -r cf54bae8ad42 test-data/glm_result01 --- a/test-data/glm_result01 Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/glm_result01 Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/glm_result02 --- a/test-data/glm_result02 Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/glm_result02 Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/glm_result08 --- a/test-data/glm_result08 Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/glm_result08 Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/keras01.json --- a/test-data/keras01.json Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/keras01.json Mon Dec 16 05:18:07 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 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/keras02.json --- a/test-data/keras02.json Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/keras02.json Mon Dec 16 05:18:07 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' |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/keras04.json --- a/test-data/keras04.json Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/keras04.json Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/keras_batch_params01.tabular --- a/test-data/keras_batch_params01.tabular Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/keras_batch_params01.tabular Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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. |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 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 05:18:07 2019 -0500 |
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| @@ -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. |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/keras_params04.tabular --- a/test-data/keras_params04.tabular Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/keras_params04.tabular Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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 da9f0b8ca61c -r cf54bae8ad42 test-data/keras_train_eval_y_true02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/keras_train_eval_y_true02.tabular Mon Dec 16 05:18:07 2019 -0500 |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/ml_vis01.html --- a/test-data/ml_vis01.html Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/ml_vis01.html Mon Dec 16 05:18:07 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 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| b |
| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/ml_vis02.html --- a/test-data/ml_vis02.html Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/ml_vis02.html Mon Dec 16 05:18:07 2019 -0500 |
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| b |
| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/ml_vis03.html --- a/test-data/ml_vis03.html Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/ml_vis03.html Mon Dec 16 05:18:07 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 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| b |
| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/ml_vis04.html --- a/test-data/ml_vis04.html Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/ml_vis04.html Mon Dec 16 05:18:07 2019 -0500 |
| [ |
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| b |
| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/ml_vis05.html --- a/test-data/ml_vis05.html Thu Nov 07 05:27:35 2019 -0500 +++ b/test-data/ml_vis05.html Mon Dec 16 05:18:07 2019 -0500 |
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"#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"x": 0.5, "xanchor": "center", "y": 0.92, "yanchor": "top"}, "xaxis": {"title": {"text": "Number of features selected"}}, "yaxis": {"title": {"text": "Cross validation score"}}},\n+ {"responsive": true}\n+ )\n+ };\n+ \n+ </script>\n+ </div>\n+</body>\n+</html>\n\\ No newline at end of file\n' |
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| @@ -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)} \ No newline at end of file +{'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)} \ No newline at end of file |
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| diff -r da9f0b8ca61c -r cf54bae8ad42 test-data/pipeline_params05.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pipeline_params05.tabular Mon Dec 16 05:18:07 2019 -0500 |
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| @@ -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. |
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| @@ -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 da9f0b8ca61c -r cf54bae8ad42 train_test_eval.py --- a/train_test_eval.py Thu Nov 07 05:27:35 2019 -0500 +++ b/train_test_eval.py Mon Dec 16 05:18:07 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', |
