# HG changeset patch
# User bgruening
# Date 1691587420 0
# Node ID 6eb4e7fb0f919297c8edac886a3fcc682bcd3777
# Parent 8a794e6d33888aefc11de665a4a32d60a8e5557c
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 association_rules.py
--- a/association_rules.py Thu Aug 11 09:49:51 2022 +0000
+++ b/association_rules.py Wed Aug 09 13:23:40 2023 +0000
@@ -7,7 +7,16 @@
from mlxtend.preprocessing import TransactionEncoder
-def main(inputs, infile, outfile, min_support=0.5, min_confidence=0.5, min_lift=1.0, min_conviction=1.0, max_length=None):
+def main(
+ inputs,
+ infile,
+ outfile,
+ min_support=0.5,
+ min_confidence=0.5,
+ min_lift=1.0,
+ min_conviction=1.0,
+ max_length=None,
+):
"""
Parameter
---------
@@ -36,13 +45,13 @@
Maximum length
"""
- warnings.simplefilter('ignore')
+ warnings.simplefilter("ignore")
- with open(inputs, 'r') as param_handler:
+ with open(inputs, "r") as param_handler:
params = json.load(param_handler)
- input_header = params['header0']
- header = 'infer' if input_header else None
+ input_header = params["header0"]
+ header = "infer" if input_header else None
with open(infile) as fp:
lines = fp.read().splitlines()
@@ -65,41 +74,45 @@
# Extract frequent itemsets for association rule mining
# use_colnames: Use DataFrames' column names in the returned DataFrame instead of column indices
- frequent_itemsets = fpgrowth(df, min_support=min_support, use_colnames=True, max_len=max_length)
+ frequent_itemsets = fpgrowth(
+ df, min_support=min_support, use_colnames=True, max_len=max_length
+ )
# Get association rules, with confidence larger than min_confidence
- rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=min_confidence)
+ rules = association_rules(
+ frequent_itemsets, metric="confidence", min_threshold=min_confidence
+ )
# Filter association rules, keeping rules with lift and conviction larger than min_liftand and min_conviction
- rules = rules[(rules['lift'] >= min_lift) & (rules['conviction'] >= min_conviction)]
+ rules = rules[(rules["lift"] >= min_lift) & (rules["conviction"] >= min_conviction)]
# Convert columns from frozenset to list (more readable)
- rules['antecedents'] = rules['antecedents'].apply(list)
- rules['consequents'] = rules['consequents'].apply(list)
+ rules["antecedents"] = rules["antecedents"].apply(list)
+ rules["consequents"] = rules["consequents"].apply(list)
# The next 3 steps are intended to fix the order of the association
# rules generated, so tests that rely on diff'ing a desired output
# with an expected output can pass
# 1) Sort entry in every row/column for columns 'antecedents' and 'consequents'
- rules['antecedents'] = rules['antecedents'].apply(lambda row: sorted(row))
- rules['consequents'] = rules['consequents'].apply(lambda row: sorted(row))
+ rules["antecedents"] = rules["antecedents"].apply(lambda row: sorted(row))
+ rules["consequents"] = rules["consequents"].apply(lambda row: sorted(row))
# 2) Create two temporary string columns to sort on
- rules['ant_str'] = rules['antecedents'].apply(lambda row: " ".join(row))
- rules['con_str'] = rules['consequents'].apply(lambda row: " ".join(row))
+ rules["ant_str"] = rules["antecedents"].apply(lambda row: " ".join(row))
+ rules["con_str"] = rules["consequents"].apply(lambda row: " ".join(row))
# 3) Sort results so they are re-producable
- rules.sort_values(by=['ant_str', 'con_str'], inplace=True)
- del rules['ant_str']
- del rules['con_str']
+ rules.sort_values(by=["ant_str", "con_str"], inplace=True)
+ del rules["ant_str"]
+ del rules["con_str"]
rules.reset_index(drop=True, inplace=True)
# Write association rules and metrics to file
rules.to_csv(outfile, sep="\t", index=False)
-if __name__ == '__main__':
+if __name__ == "__main__":
aparser = argparse.ArgumentParser()
aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
aparser.add_argument("-y", "--infile", dest="infile", required=True)
@@ -111,6 +124,13 @@
aparser.add_argument("-t", "--length", dest="length", default=5)
args = aparser.parse_args()
- main(args.inputs, args.infile, args.outfile,
- min_support=float(args.support), min_confidence=float(args.confidence),
- min_lift=float(args.lift), min_conviction=float(args.conviction), max_length=int(args.length))
+ main(
+ args.inputs,
+ args.infile,
+ args.outfile,
+ min_support=float(args.support),
+ min_confidence=float(args.confidence),
+ min_lift=float(args.lift),
+ min_conviction=float(args.conviction),
+ max_length=int(args.length),
+ )
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 fitted_model_eval.py
--- a/fitted_model_eval.py Thu Aug 11 09:49:51 2022 +0000
+++ b/fitted_model_eval.py Wed Aug 09 13:23:40 2023 +0000
@@ -3,11 +3,11 @@
import warnings
import pandas as pd
-from galaxy_ml.utils import get_scoring, load_model, read_columns
+from galaxy_ml.model_persist import load_model_from_h5
+from galaxy_ml.utils import clean_params, get_scoring, read_columns
from scipy.io import mmread
-from sklearn.metrics.scorer import _check_multimetric_scoring
+from sklearn.metrics._scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score
-from sklearn.pipeline import Pipeline
def _get_X_y(params, infile1, infile2):
@@ -75,7 +75,12 @@
loaded_df[df_key] = infile2
y = read_columns(
- infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
+ infile2,
+ c=c,
+ c_option=column_option,
+ sep="\t",
+ header=header,
+ parse_dates=True,
)
if len(y.shape) == 2 and y.shape[1] == 1:
y = y.ravel()
@@ -83,14 +88,7 @@
return X, y
-def main(
- inputs,
- infile_estimator,
- outfile_eval,
- infile_weights=None,
- infile1=None,
- infile2=None,
-):
+def main(inputs, infile_estimator, outfile_eval, infile1=None, infile2=None):
"""
Parameter
---------
@@ -103,9 +101,6 @@
outfile_eval : str
File path to save the evalulation results, tabular
- infile_weights : str
- File path to weights input
-
infile1 : str
File path to dataset containing features
@@ -120,40 +115,20 @@
X_test, y_test = _get_X_y(params, infile1, infile2)
# load model
- with open(infile_estimator, "rb") as est_handler:
- estimator = load_model(est_handler)
-
- main_est = estimator
- if isinstance(estimator, Pipeline):
- main_est = estimator.steps[-1][-1]
- if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
- if not infile_weights or infile_weights == "None":
- raise ValueError(
- "The selected model skeleton asks for weights, "
- "but no dataset for weights was provided!"
- )
- main_est.load_weights(infile_weights)
+ estimator = load_model_from_h5(infile_estimator)
+ estimator = clean_params(estimator)
# handle scorer, convert to scorer dict
- # Check if scoring is specified
scoring = params["scoring"]
- if scoring is not None:
- # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
- # Check if secondary_scoring is specified
- secondary_scoring = scoring.get("secondary_scoring", None)
- if secondary_scoring is not None:
- # If secondary_scoring is specified, convert the list into comman separated string
- scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])
-
scorer = get_scoring(scoring)
- scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+ if not isinstance(scorer, (dict, list)):
+ scorer = [scoring["primary_scoring"]]
+ scorer = _check_multimetric_scoring(estimator, scoring=scorer)
if hasattr(estimator, "evaluate"):
- scores = estimator.evaluate(
- X_test, y_test=y_test, scorer=scorer, is_multimetric=True
- )
+ scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer)
else:
- scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
+ scores = _score(estimator, X_test, y_test, scorer)
# handle output
for name, score in scores.items():
@@ -167,7 +142,6 @@
aparser = argparse.ArgumentParser()
aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
- aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
aparser.add_argument("-X", "--infile1", dest="infile1")
aparser.add_argument("-y", "--infile2", dest="infile2")
aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
@@ -177,7 +151,6 @@
args.inputs,
args.infile_estimator,
args.outfile_eval,
- infile_weights=args.infile_weights,
infile1=args.infile1,
infile2=args.infile2,
)
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 keras_deep_learning.py
--- a/keras_deep_learning.py Thu Aug 11 09:49:51 2022 +0000
+++ b/keras_deep_learning.py Wed Aug 09 13:23:40 2023 +0000
@@ -1,21 +1,19 @@
import argparse
import json
-import pickle
import warnings
from ast import literal_eval
-import keras
-import pandas as pd
import six
-from galaxy_ml.utils import get_search_params, SafeEval, try_get_attr
-from keras.models import Model, Sequential
+from galaxy_ml.model_persist import dump_model_to_h5
+from galaxy_ml.utils import SafeEval, try_get_attr
+from tensorflow import keras
+from tensorflow.keras.models import Model, Sequential
safe_eval = SafeEval()
def _handle_shape(literal):
- """
- Eval integer or list/tuple of integers from string
+ """Eval integer or list/tuple of integers from string
Parameters:
-----------
@@ -32,8 +30,7 @@
def _handle_regularizer(literal):
- """
- Construct regularizer from string literal
+ """Construct regularizer from string literal
Parameters
----------
@@ -57,8 +54,7 @@
def _handle_constraint(config):
- """
- Construct constraint from galaxy tool parameters.
+ """Construct constraint from galaxy tool parameters.
Suppose correct dictionary format
Parameters
@@ -91,9 +87,7 @@
def _handle_layer_parameters(params):
- """
- Access to handle all kinds of parameters
- """
+ """Access to handle all kinds of parameters"""
for key, value in six.iteritems(params):
if value in ("None", ""):
params[key] = None
@@ -104,28 +98,24 @@
):
continue
- if (
- key
- in [
- "input_shape",
- "noise_shape",
- "shape",
- "batch_shape",
- "target_shape",
- "dims",
- "kernel_size",
- "strides",
- "dilation_rate",
- "output_padding",
- "cropping",
- "size",
- "padding",
- "pool_size",
- "axis",
- "shared_axes",
- ]
- and isinstance(value, str)
- ):
+ if key in [
+ "input_shape",
+ "noise_shape",
+ "shape",
+ "batch_shape",
+ "target_shape",
+ "dims",
+ "kernel_size",
+ "strides",
+ "dilation_rate",
+ "output_padding",
+ "cropping",
+ "size",
+ "padding",
+ "pool_size",
+ "axis",
+ "shared_axes",
+ ] and isinstance(value, str):
params[key] = _handle_shape(value)
elif key.endswith("_regularizer") and isinstance(value, dict):
@@ -141,8 +131,7 @@
def get_sequential_model(config):
- """
- Construct keras Sequential model from Galaxy tool parameters
+ """Construct keras Sequential model from Galaxy tool parameters
Parameters:
-----------
@@ -165,7 +154,7 @@
options.update(kwargs)
# add input_shape to the first layer only
- if not getattr(model, "_layers") and input_shape is not None:
+ if not model.get_config()["layers"] and input_shape is not None:
options["input_shape"] = input_shape
model.add(klass(**options))
@@ -174,8 +163,7 @@
def get_functional_model(config):
- """
- Construct keras functional model from Galaxy tool parameters
+ """Construct keras functional model from Galaxy tool parameters
Parameters
-----------
@@ -221,8 +209,7 @@
def get_batch_generator(config):
- """
- Construct keras online data generator from Galaxy tool parameters
+ """Construct keras online data generator from Galaxy tool parameters
Parameters
-----------
@@ -246,8 +233,7 @@
def config_keras_model(inputs, outfile):
- """
- config keras model layers and output JSON
+ """config keras model layers and output JSON
Parameters
----------
@@ -271,16 +257,8 @@
json.dump(json.loads(json_string), f, indent=2)
-def build_keras_model(
- inputs,
- outfile,
- model_json,
- infile_weights=None,
- batch_mode=False,
- outfile_params=None,
-):
- """
- for `keras_model_builder` tool
+def build_keras_model(inputs, outfile, model_json, batch_mode=False):
+ """for `keras_model_builder` tool
Parameters
----------
@@ -290,12 +268,8 @@
Path to galaxy dataset containing the keras_galaxy model output.
model_json : str
Path to dataset containing keras model JSON.
- infile_weights : str or None
- If string, path to dataset containing model weights.
batch_mode : bool, default=False
Whether to build online batch classifier.
- outfile_params : str, default=None
- File path to search parameters output.
"""
with open(model_json, "r") as f:
json_model = json.load(f)
@@ -307,7 +281,7 @@
if json_model["class_name"] == "Sequential":
options["model_type"] = "sequential"
klass = Sequential
- elif json_model["class_name"] == "Model":
+ elif json_model["class_name"] == "Functional":
options["model_type"] = "functional"
klass = Model
else:
@@ -315,8 +289,9 @@
# load prefitted model
if inputs["mode_selection"]["mode_type"] == "prefitted":
- estimator = klass.from_config(config)
- estimator.load_weights(infile_weights)
+ # estimator = klass.from_config(config)
+ # estimator.load_weights(infile_weights)
+ raise Exception("Prefitted was deprecated!")
# build train model
else:
cls_name = inputs["mode_selection"]["learning_type"]
@@ -338,8 +313,10 @@
)
train_metrics = inputs["mode_selection"]["compile_params"]["metrics"]
+ if not isinstance(train_metrics, list): # for older galaxy
+ train_metrics = train_metrics.split(",")
if train_metrics[-1] == "none":
- train_metrics = train_metrics[:-1]
+ train_metrics.pop()
options["metrics"] = train_metrics
options.update(inputs["mode_selection"]["fit_params"])
@@ -355,19 +332,10 @@
"class_positive_factor"
]
estimator = klass(config, **options)
- if outfile_params:
- hyper_params = get_search_params(estimator)
- # TODO: remove this after making `verbose` tunable
- for h_param in hyper_params:
- if h_param[1].endswith("verbose"):
- h_param[0] = "@"
- df = pd.DataFrame(hyper_params, columns=["", "Parameter", "Value"])
- df.to_csv(outfile_params, sep="\t", index=False)
print(repr(estimator))
- # save model by pickle
- with open(outfile, "wb") as f:
- pickle.dump(estimator, f, pickle.HIGHEST_PROTOCOL)
+ # save model
+ dump_model_to_h5(estimator, outfile, verbose=1)
if __name__ == "__main__":
@@ -377,9 +345,7 @@
aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
aparser.add_argument("-m", "--model_json", dest="model_json")
aparser.add_argument("-t", "--tool_id", dest="tool_id")
- aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
aparser.add_argument("-o", "--outfile", dest="outfile")
- aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
args = aparser.parse_args()
input_json_path = args.inputs
@@ -388,9 +354,7 @@
tool_id = args.tool_id
outfile = args.outfile
- outfile_params = args.outfile_params
model_json = args.model_json
- infile_weights = args.infile_weights
# for keras_model_config tool
if tool_id == "keras_model_config":
@@ -403,10 +367,5 @@
batch_mode = True
build_keras_model(
- inputs=inputs,
- model_json=model_json,
- infile_weights=infile_weights,
- batch_mode=batch_mode,
- outfile=outfile,
- outfile_params=outfile_params,
+ inputs=inputs, model_json=model_json, batch_mode=batch_mode, outfile=outfile
)
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 keras_macros.xml
--- a/keras_macros.xml Thu Aug 11 09:49:51 2022 +0000
+++ b/keras_macros.xml Wed Aug 09 13:23:40 2023 +0000
@@ -1,26 +1,27 @@
- 0.5.0
+ 2.10.0
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+ help="(l1, l2). l1/l2: float; L1/l2 regularization factor. (0., 0.) is equivalent to `None`" />
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+ Indexing starts at 1. For instance, (2, 1) permutes the first and second dimension of the input." />
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 keras_model_config.xml
--- a/keras_model_config.xml Thu Aug 11 09:49:51 2022 +0000
+++ b/keras_model_config.xml Wed Aug 09 13:23:40 2023 +0000
@@ -1,778 +1,879 @@
-
- using Keras
-
- main_macros.xml
- keras_macros.xml
-
-
-
- echo "@KERAS_VERSION@"
-
-
+ using Keras
+
+ main_macros.xml
+ keras_macros.xml
+
+
+
+ echo "@VERSION@"
+
+
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 keras_train_and_eval.py
--- a/keras_train_and_eval.py Thu Aug 11 09:49:51 2022 +0000
+++ b/keras_train_and_eval.py Wed Aug 09 13:23:40 2023 +0000
@@ -1,34 +1,43 @@
import argparse
import json
import os
-import pickle
import warnings
from itertools import chain
import joblib
import numpy as np
import pandas as pd
-from galaxy_ml.externals.selene_sdk.utils import compute_score
-from galaxy_ml.keras_galaxy_models import _predict_generator
+from galaxy_ml.keras_galaxy_models import (
+ _predict_generator,
+ KerasGBatchClassifier,
+)
+from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
from galaxy_ml.model_validations import train_test_split
-from galaxy_ml.utils import (clean_params, get_main_estimator,
- get_module, get_scoring, load_model, read_columns,
- SafeEval, try_get_attr)
+from galaxy_ml.utils import (
+ clean_params,
+ gen_compute_scores,
+ get_main_estimator,
+ get_module,
+ get_scoring,
+ read_columns,
+ SafeEval
+)
from scipy.io import mmread
-from sklearn.metrics.scorer import _check_multimetric_scoring
-from sklearn.model_selection import _search, _validation
+from sklearn.metrics._scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score
-from sklearn.pipeline import Pipeline
-from sklearn.utils import indexable, safe_indexing
-
-_fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
-setattr(_search, "_fit_and_score", _fit_and_score)
-setattr(_validation, "_fit_and_score", _fit_and_score)
+from sklearn.utils import _safe_indexing, indexable
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")
+NON_SEARCHABLE = (
+ "n_jobs",
+ "pre_dispatch",
+ "memory",
+ "_path",
+ "_dir",
+ "nthread",
+ "callbacks",
+)
ALLOWED_CALLBACKS = (
"EarlyStopping",
"TerminateOnNaN",
@@ -96,7 +105,7 @@
train = index_arr[~np.isin(groups, group_names)]
rval = list(
chain.from_iterable(
- (safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays
+ (_safe_indexing(a, train), _safe_indexing(a, test)) for a in new_arrays
)
)
else:
@@ -108,68 +117,69 @@
return rval
-def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):
- """output scores based on input scorer
+def _evaluate_keras_and_sklearn_scores(
+ estimator,
+ data_generator,
+ X,
+ y=None,
+ sk_scoring=None,
+ steps=None,
+ batch_size=32,
+ return_predictions=False,
+):
+ """output scores for bother keras and sklearn metrics
Parameters
- ----------
- y_true : array
- True label or target values
- pred_probas : array
- Prediction values, probability for classification problem
- scorer : dict
- dict of `sklearn.metrics.scorer.SCORER`
- is_multimetric : bool, default is True
+ -----------
+ estimator : object
+ Fitted `galaxy_ml.keras_galaxy_models.KerasGBatchClassifier`.
+ data_generator : object
+ From `galaxy_ml.preprocessors.ImageDataFrameBatchGenerator`.
+ X : 2-D array
+ Contains indecies of images that need to be evaluated.
+ y : None
+ Target value.
+ sk_scoring : dict
+ Galaxy tool input parameters.
+ steps : integer or None
+ Evaluation/prediction steps before stop.
+ batch_size : integer
+ Number of samples in a batch
+ return_predictions : bool, default is False
+ Whether to return predictions and true labels.
"""
- if y_true.ndim == 1 or y_true.shape[-1] == 1:
- pred_probas = pred_probas.ravel()
- pred_labels = (pred_probas > 0.5).astype("int32")
- targets = y_true.ravel().astype("int32")
- if not is_multimetric:
- preds = (
- pred_labels
- if scorer.__class__.__name__ == "_PredictScorer"
- else pred_probas
- )
- score = scorer._score_func(targets, preds, **scorer._kwargs)
+ scores = {}
- return score
- else:
- scores = {}
- for name, one_scorer in scorer.items():
- preds = (
- pred_labels
- if one_scorer.__class__.__name__ == "_PredictScorer"
- else pred_probas
- )
- score = one_scorer._score_func(targets, preds, **one_scorer._kwargs)
- scores[name] = score
-
- # TODO: multi-class metrics
- # multi-label
+ generator = data_generator.flow(X, y=y, batch_size=batch_size)
+ # keras metrics evaluation
+ # handle scorer, convert to scorer dict
+ generator.reset()
+ score_results = estimator.model_.evaluate_generator(generator, steps=steps)
+ metrics_names = estimator.model_.metrics_names
+ if not isinstance(metrics_names, list):
+ scores[metrics_names] = score_results
else:
- pred_labels = (pred_probas > 0.5).astype("int32")
- targets = y_true.astype("int32")
- if not is_multimetric:
- preds = (
- pred_labels
- if scorer.__class__.__name__ == "_PredictScorer"
- else pred_probas
- )
- score, _ = compute_score(preds, targets, scorer._score_func)
- return score
- else:
- scores = {}
- for name, one_scorer in scorer.items():
- preds = (
- pred_labels
- if one_scorer.__class__.__name__ == "_PredictScorer"
- else pred_probas
- )
- score, _ = compute_score(preds, targets, one_scorer._score_func)
- scores[name] = score
+ scores = dict(zip(metrics_names, score_results))
+
+ if sk_scoring["primary_scoring"] == "default" and not return_predictions:
+ return scores
+
+ generator.reset()
+ predictions, y_true = _predict_generator(estimator.model_, generator, steps=steps)
- return scores
+ # for sklearn metrics
+ if sk_scoring["primary_scoring"] != "default":
+ scorer = get_scoring(sk_scoring)
+ if not isinstance(scorer, (dict, list)):
+ scorer = [sk_scoring["primary_scoring"]]
+ scorer = _check_multimetric_scoring(estimator, scoring=scorer)
+ sk_scores = gen_compute_scores(y_true, predictions, scorer)
+ scores.update(sk_scores)
+
+ if return_predictions:
+ return scores, predictions, y_true
+ else:
+ return scores, None, None
def main(
@@ -179,7 +189,6 @@
infile2,
outfile_result,
outfile_object=None,
- outfile_weights=None,
outfile_y_true=None,
outfile_y_preds=None,
groups=None,
@@ -192,46 +201,43 @@
Parameter
---------
inputs : str
- File path to galaxy tool parameter
+ File path to galaxy tool parameter.
infile_estimator : str
- File path to estimator
+ File path to estimator.
infile1 : str
- File path to dataset containing features
+ File path to dataset containing features.
infile2 : str
- File path to dataset containing target values
+ File path to dataset containing target values.
outfile_result : str
- File path to save the results, either cv_results or test result
+ File path to save the results, either cv_results or test result.
outfile_object : str, optional
- File path to save searchCV object
-
- outfile_weights : str, optional
- File path to save deep learning model weights
+ File path to save searchCV object.
outfile_y_true : str, optional
- File path to target values for prediction
+ File path to target values for prediction.
outfile_y_preds : str, optional
- File path to save deep learning model weights
+ File path to save predictions.
groups : str
- File path to dataset containing groups labels
+ File path to dataset containing groups labels.
ref_seq : str
- File path to dataset containing genome sequence file
+ File path to dataset containing genome sequence file.
intervals : str
- File path to dataset containing interval file
+ File path to dataset containing interval file.
targets : str
- File path to dataset compressed target bed file
+ File path to dataset compressed target bed file.
fasta_path : str
- File path to dataset containing fasta file
+ File path to dataset containing fasta file.
"""
warnings.simplefilter("ignore")
@@ -239,8 +245,7 @@
params = json.load(param_handler)
# load estimator
- with open(infile_estimator, "rb") as estimator_handler:
- estimator = load_model(estimator_handler)
+ estimator = load_model_from_h5(infile_estimator)
estimator = clean_params(estimator)
@@ -333,7 +338,12 @@
loaded_df[df_key] = infile2
y = read_columns(
- infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
+ infile2,
+ c=c,
+ c_option=column_option,
+ sep="\t",
+ header=header,
+ parse_dates=True,
)
if len(y.shape) == 2 and y.shape[1] == 1:
y = y.ravel()
@@ -387,16 +397,10 @@
# handle scorer, convert to scorer dict
scoring = params["experiment_schemes"]["metrics"]["scoring"]
- if scoring is not None:
- # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
- # Check if secondary_scoring is specified
- secondary_scoring = scoring.get("secondary_scoring", None)
- if secondary_scoring is not None:
- # If secondary_scoring is specified, convert the list into comman separated string
- scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])
-
scorer = get_scoring(scoring)
- scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
+ if not isinstance(scorer, (dict, list)):
+ scorer = [scoring["primary_scoring"]]
+ scorer = _check_multimetric_scoring(estimator, scoring=scorer)
# handle test (first) split
test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]
@@ -411,14 +415,9 @@
"Stratified shuffle split is not " "applicable on empty target values!"
)
- (
- X_train,
- X_test,
- y_train,
- y_test,
- groups_train,
- _groups_test,
- ) = train_test_split_none(X, y, groups, **test_split_options)
+ X_train, X_test, y_train, y_test, groups_train, groups_test = train_test_split_none(
+ X, y, groups, **test_split_options
+ )
exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]
@@ -443,11 +442,11 @@
y_train,
y_val,
groups_train,
- _groups_val,
+ groups_val,
) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)
# train and eval
- if hasattr(estimator, "validation_data"):
+ if hasattr(estimator, "config") and hasattr(estimator, "model_type"):
if exp_scheme == "train_val_test":
estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
else:
@@ -455,25 +454,46 @@
else:
estimator.fit(X_train, y_train)
- if hasattr(estimator, "evaluate"):
+ if isinstance(estimator, KerasGBatchClassifier):
+ scores = {}
steps = estimator.prediction_steps
batch_size = estimator.batch_size
- generator = estimator.data_generator_.flow(
- X_test, y=y_test, batch_size=batch_size
+ data_generator = estimator.data_generator_
+
+ scores, predictions, y_true = _evaluate_keras_and_sklearn_scores(
+ estimator,
+ data_generator,
+ X_test,
+ y=y_test,
+ sk_scoring=scoring,
+ steps=steps,
+ batch_size=batch_size,
+ return_predictions=bool(outfile_y_true),
)
- predictions, y_true = _predict_generator(
- estimator.model_, generator, steps=steps
- )
- scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)
else:
+ scores = {}
+ if hasattr(estimator, "model_") and hasattr(estimator.model_, "metrics_names"):
+ batch_size = estimator.batch_size
+ score_results = estimator.model_.evaluate(
+ X_test, y=y_test, batch_size=batch_size, verbose=0
+ )
+ metrics_names = estimator.model_.metrics_names
+ if not isinstance(metrics_names, list):
+ scores[metrics_names] = score_results
+ else:
+ scores = dict(zip(metrics_names, score_results))
+
if hasattr(estimator, "predict_proba"):
predictions = estimator.predict_proba(X_test)
else:
predictions = estimator.predict(X_test)
y_true = y_test
- scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
+ sk_scores = _score(estimator, X_test, y_test, scorer)
+ scores.update(sk_scores)
+
+ # handle output
if outfile_y_true:
try:
pd.DataFrame(y_true).to_csv(outfile_y_true, sep="\t", index=False)
@@ -486,7 +506,6 @@
)
except Exception as e:
print("Error in saving predictions: %s" % e)
-
# handle output
for name, score in scores.items():
scores[name] = [score]
@@ -497,23 +516,7 @@
memory.clear(warn=False)
if outfile_object:
- main_est = estimator
- if isinstance(estimator, Pipeline):
- main_est = estimator.steps[-1][-1]
-
- if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
- if outfile_weights:
- main_est.save_weights(outfile_weights)
- del main_est.model_
- del main_est.fit_params
- del main_est.model_class_
- if getattr(main_est, "validation_data", None):
- del main_est.validation_data
- if getattr(main_est, "data_generator_", None):
- del main_est.data_generator_
-
- with open(outfile_object, "wb") as output_handler:
- pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
+ dump_model_to_h5(estimator, outfile_object)
if __name__ == "__main__":
@@ -524,7 +527,6 @@
aparser.add_argument("-y", "--infile2", dest="infile2")
aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
- aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
aparser.add_argument("-g", "--groups", dest="groups")
@@ -541,7 +543,6 @@
args.infile2,
args.outfile_result,
outfile_object=args.outfile_object,
- outfile_weights=args.outfile_weights,
outfile_y_true=args.outfile_y_true,
outfile_y_preds=args.outfile_y_preds,
groups=args.groups,
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 label_encoder.py
--- a/label_encoder.py Thu Aug 11 09:49:51 2022 +0000
+++ b/label_encoder.py Wed Aug 09 13:23:40 2023 +0000
@@ -21,24 +21,24 @@
File path to output vector
"""
- warnings.simplefilter('ignore')
+ warnings.simplefilter("ignore")
- with open(inputs, 'r') as param_handler:
+ with open(inputs, "r") as param_handler:
params = json.load(param_handler)
- input_header = params['header0']
- header = 'infer' if input_header else None
+ input_header = params["header0"]
+ header = "infer" if input_header else None
- input_vector = pd.read_csv(infile, sep='\t', header=header)
+ input_vector = pd.read_csv(infile, sep="\t", header=header)
le = LabelEncoder()
output_vector = le.fit_transform(input_vector)
- np.savetxt(outfile, output_vector, fmt="%d", delimiter='\t')
+ np.savetxt(outfile, output_vector, fmt="%d", delimiter="\t")
-if __name__ == '__main__':
+if __name__ == "__main__":
aparser = argparse.ArgumentParser()
aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
aparser.add_argument("-y", "--infile", dest="infile")
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 main_macros.xml
--- a/main_macros.xml Thu Aug 11 09:49:51 2022 +0000
+++ b/main_macros.xml Wed Aug 09 13:23:40 2023 +0000
@@ -1,225 +1,228 @@
- 1.0.8.4
+ 1.0.10.0
+ 21.05
- Galaxy-ML
+ python
+ galaxy-ml
-
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-
-
-
- selected_tasks['selected_task'] == 'load'
-
-
- selected_tasks['selected_task'] == 'train'
-
-
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+
+
+
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+
-
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-
- 10.5281/zenodo.15094
-
-
+
+
+
+
+
+
+
+
+
+
+ selected_tasks['selected_task'] == 'load'
+
+
+ selected_tasks['selected_task'] == 'train'
+
+
+
-
-
- 10.1371/journal.pcbi.1009014
-
- @article{JMLR:v12:pedregosa11a,
- title = {Scikit-learn: Machine Learning in {P}ython},
- author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
- and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
- and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
- Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
- journal = {Journal of Machine Learning Research},
- volume = {12},
- pages = {2825--2830},
- year = {2011}
- url = {http://jmlr.org/papers/v12/pedregosa11a.html}
- }
-
-
-
-
+
+
+
+ 10.5281/zenodo.15094
+
+
-
-
-
- @Misc{,
- author = {Eric Jones and Travis Oliphant and Pearu Peterson and others},
- title = {{SciPy}: Open source scientific tools for {Python}},
- year = {2001--},
- url = {http://www.scipy.org/},
- note = {[Online; accessed 2016-04-09]}
+
+
+
+ @article{scikit-learn,
+ title={Scikit-learn: Machine Learning in {P}ython},
+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+ and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+ and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+ Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+ journal={Journal of Machine Learning Research},
+ volume={12},
+ pages={2825--2830},
+ year={2011}
}
-
-
-
+
+
+
+
-
-
+
+
+
+ @Misc{,
+ author = {Eric Jones and Travis Oliphant and Pearu Peterson and others},
+ title = {{SciPy}: Open source scientific tools for {Python}},
+ year = {2001--},
+ url = "http://www.scipy.org/",
+ note = {[Online; accessed 2016-04-09]}
+ }
+
+
+
+
+
+
@article{DBLP:journals/corr/abs-1711-08477,
author = {Ryan J. Urbanowicz and
Randal S. Olson and
@@ -1973,33 +1953,33 @@
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1711-08477},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
-
-
+
+
-
-
+
+
@inproceedings{Chen:2016:XST:2939672.2939785,
- author = {Chen, Tianqi and Guestrin, Carlos},
- title = {{XGBoost}: A Scalable Tree Boosting System},
+ author = {Chen, Tianqi and Guestrin, Carlos},
+ title = {{XGBoost}: A Scalable Tree Boosting System},
booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
- series = {KDD '16},
- year = {2016},
- isbn = {978-1-4503-4232-2},
- location = {San Francisco, California, USA},
- pages = {785--794},
- numpages = {10},
- url = {http://doi.acm.org/10.1145/2939672.2939785},
- doi = {10.1145/2939672.2939785},
- acmid = {2939785},
+ series = {KDD '16},
+ year = {2016},
+ isbn = {978-1-4503-4232-2},
+ location = {San Francisco, California, USA},
+ pages = {785--794},
+ numpages = {10},
+ url = {http://doi.acm.org/10.1145/2939672.2939785},
+ doi = {10.1145/2939672.2939785},
+ acmid = {2939785},
publisher = {ACM},
- address = {New York, NY, USA},
- keywords = {large-scale machine learning},
+ address = {New York, NY, USA},
+ keywords = {large-scale machine learning},
}
-
-
+
+
-
-
+
+
@article{JMLR:v18:16-365,
author = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
@@ -2010,11 +1990,22 @@
pages = {1-5},
url = {http://jmlr.org/papers/v18/16-365.html}
}
-
-
+
+
-
- 10.1038/s41592-019-0360-8
-
+
+
+ @article{chen2019selene,
+ title={Selene: a PyTorch-based deep learning library for sequence data},
+ author={Chen, Kathleen M and Cofer, Evan M and Zhou, Jian and Troyanskaya, Olga G},
+ journal={Nature methods},
+ volume={16},
+ number={4},
+ pages={315},
+ year={2019},
+ publisher={Nature Publishing Group}
+ }
+
+
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 ml_visualization_ex.py
--- a/ml_visualization_ex.py Thu Aug 11 09:49:51 2022 +0000
+++ b/ml_visualization_ex.py Wed Aug 09 13:23:40 2023 +0000
@@ -9,13 +9,18 @@
import pandas as pd
import plotly
import plotly.graph_objs as go
-from galaxy_ml.utils import load_model, read_columns, SafeEval
-from keras.models import model_from_json
-from keras.utils import plot_model
-from sklearn.feature_selection.base import SelectorMixin
-from sklearn.metrics import (auc, average_precision_score, confusion_matrix,
- precision_recall_curve, roc_curve)
+from galaxy_ml.model_persist import load_model_from_h5
+from galaxy_ml.utils import read_columns, SafeEval
+from sklearn.feature_selection._base import SelectorMixin
+from sklearn.metrics import (
+ auc,
+ average_precision_score,
+ precision_recall_curve,
+ roc_curve,
+)
from sklearn.pipeline import Pipeline
+from tensorflow.keras.models import model_from_json
+from tensorflow.keras.utils import plot_model
safe_eval = SafeEval()
@@ -253,30 +258,6 @@
os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output"))
-def get_dataframe(file_path, plot_selection, header_name, column_name):
- header = "infer" if plot_selection[header_name] else None
- column_option = plot_selection[column_name]["selected_column_selector_option"]
- if column_option in [
- "by_index_number",
- "all_but_by_index_number",
- "by_header_name",
- "all_but_by_header_name",
- ]:
- col = plot_selection[column_name]["col1"]
- else:
- col = None
- _, input_df = read_columns(
- file_path,
- c=col,
- c_option=column_option,
- return_df=True,
- sep="\t",
- header=header,
- parse_dates=True,
- )
- return input_df
-
-
def main(
inputs,
infile_estimator=None,
@@ -290,10 +271,6 @@
targets=None,
fasta_path=None,
model_config=None,
- true_labels=None,
- predicted_labels=None,
- plot_color=None,
- title=None,
):
"""
Parameter
@@ -334,18 +311,6 @@
model_config : str, default is None
File path to dataset containing JSON config for neural networks
-
- true_labels : str, default is None
- File path to dataset containing true labels
-
- predicted_labels : str, default is None
- File path to dataset containing true predicted labels
-
- plot_color : str, default is None
- Color of the confusion matrix heatmap
-
- title : str, default is None
- Title of the confusion matrix heatmap
"""
warnings.simplefilter("ignore")
@@ -357,8 +322,7 @@
plot_format = params["plotting_selection"]["plot_format"]
if plot_type == "feature_importances":
- with open(infile_estimator, "rb") as estimator_handler:
- estimator = load_model(estimator_handler)
+ estimator = load_model_from_h5(infile_estimator)
column_option = params["plotting_selection"]["column_selector_options"][
"selected_column_selector_option"
@@ -570,36 +534,6 @@
return 0
- elif plot_type == "classification_confusion_matrix":
- plot_selection = params["plotting_selection"]
- input_true = get_dataframe(
- true_labels, plot_selection, "header_true", "column_selector_options_true"
- )
- header_predicted = "infer" if plot_selection["header_predicted"] else None
- input_predicted = pd.read_csv(
- predicted_labels, sep="\t", parse_dates=True, header=header_predicted
- )
- true_classes = input_true.iloc[:, -1].copy()
- predicted_classes = input_predicted.iloc[:, -1].copy()
- axis_labels = list(set(true_classes))
- c_matrix = confusion_matrix(true_classes, predicted_classes)
- fig, ax = plt.subplots(figsize=(7, 7))
- im = plt.imshow(c_matrix, cmap=plot_color)
- for i in range(len(c_matrix)):
- for j in range(len(c_matrix)):
- ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k")
- ax.set_ylabel("True class labels")
- ax.set_xlabel("Predicted class labels")
- ax.set_title(title)
- ax.set_xticks(axis_labels)
- ax.set_yticks(axis_labels)
- fig.colorbar(im, ax=ax)
- fig.tight_layout()
- plt.savefig("output.png", dpi=125)
- os.rename("output.png", "output")
-
- return 0
-
# save pdf file to disk
# fig.write_image("image.pdf", format='pdf')
# fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2)
@@ -619,10 +553,6 @@
aparser.add_argument("-t", "--targets", dest="targets")
aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
aparser.add_argument("-c", "--model_config", dest="model_config")
- aparser.add_argument("-tl", "--true_labels", dest="true_labels")
- aparser.add_argument("-pl", "--predicted_labels", dest="predicted_labels")
- aparser.add_argument("-pc", "--plot_color", dest="plot_color")
- aparser.add_argument("-pt", "--title", dest="title")
args = aparser.parse_args()
main(
@@ -638,8 +568,4 @@
targets=args.targets,
fasta_path=args.fasta_path,
model_config=args.model_config,
- true_labels=args.true_labels,
- predicted_labels=args.predicted_labels,
- plot_color=args.plot_color,
- title=args.title,
)
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 model_prediction.py
--- a/model_prediction.py Thu Aug 11 09:49:51 2022 +0000
+++ b/model_prediction.py Wed Aug 09 13:23:40 2023 +0000
@@ -4,9 +4,10 @@
import numpy as np
import pandas as pd
-from galaxy_ml.utils import get_module, load_model, read_columns, try_get_attr
+from galaxy_ml.model_persist import load_model_from_h5
+from galaxy_ml.utils import (clean_params, get_module, read_columns,
+ try_get_attr)
from scipy.io import mmread
-from sklearn.pipeline import Pipeline
N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
@@ -15,7 +16,6 @@
inputs,
infile_estimator,
outfile_predict,
- infile_weights=None,
infile1=None,
fasta_path=None,
ref_seq=None,
@@ -27,15 +27,12 @@
inputs : str
File path to galaxy tool parameter
- infile_estimator : strgit
+ infile_estimator : str
File path to trained estimator input
outfile_predict : str
File path to save the prediction results, tabular
- infile_weights : str
- File path to weights input
-
infile1 : str
File path to dataset containing features
@@ -54,19 +51,8 @@
params = json.load(param_handler)
# load model
- with open(infile_estimator, "rb") as est_handler:
- estimator = load_model(est_handler)
-
- main_est = estimator
- if isinstance(estimator, Pipeline):
- main_est = estimator.steps[-1][-1]
- if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
- if not infile_weights or infile_weights == "None":
- raise ValueError(
- "The selected model skeleton asks for weights, "
- "but dataset for weights wan not selected!"
- )
- main_est.load_weights(infile_weights)
+ estimator = load_model_from_h5(infile_estimator)
+ estimator = clean_params(estimator)
# handle data input
input_type = params["input_options"]["selected_input"]
@@ -221,7 +207,6 @@
aparser = argparse.ArgumentParser()
aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
- aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
aparser.add_argument("-X", "--infile1", dest="infile1")
aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
@@ -233,7 +218,6 @@
args.inputs,
args.infile_estimator,
args.outfile_predict,
- infile_weights=args.infile_weights,
infile1=args.infile1,
fasta_path=args.fasta_path,
ref_seq=args.ref_seq,
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 pdb70_cs219.ffdata
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/pdb70_cs219.ffdata Wed Aug 09 13:23:40 2023 +0000
@@ -0,0 +1,189 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Galaxy
+ | Europe
+ |
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ You are not allowed to access this dataset
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 search_model_validation.py
--- a/search_model_validation.py Thu Aug 11 09:49:51 2022 +0000
+++ b/search_model_validation.py Wed Aug 09 13:23:40 2023 +0000
@@ -1,35 +1,55 @@
import argparse
-import collections
import json
import os
-import pickle
import sys
import warnings
+from distutils.version import LooseVersion as Version
import imblearn
import joblib
import numpy as np
import pandas as pd
import skrebate
-from galaxy_ml.utils import (clean_params, get_cv,
- get_main_estimator, get_module, get_scoring,
- load_model, read_columns, SafeEval, try_get_attr)
+from galaxy_ml import __version__ as galaxy_ml_version
+from galaxy_ml.binarize_target import IRAPSClassifier
+from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
+from galaxy_ml.utils import (
+ clean_params,
+ get_cv,
+ get_main_estimator,
+ get_module,
+ get_scoring,
+ read_columns,
+ SafeEval,
+ try_get_attr
+)
from scipy.io import mmread
-from sklearn import (cluster, decomposition, feature_selection,
- kernel_approximation, model_selection, preprocessing)
+from sklearn import (
+ cluster,
+ decomposition,
+ feature_selection,
+ kernel_approximation,
+ model_selection,
+ preprocessing,
+)
from sklearn.exceptions import FitFailedWarning
from sklearn.model_selection import _search, _validation
from sklearn.model_selection._validation import _score, cross_validate
-
-_fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
-setattr(_search, "_fit_and_score", _fit_and_score)
-setattr(_validation, "_fit_and_score", _fit_and_score)
+from sklearn.preprocessing import LabelEncoder
+from skopt import BayesSearchCV
N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1))
# handle disk cache
CACHE_DIR = os.path.join(os.getcwd(), "cached")
-del os
-NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks")
+NON_SEARCHABLE = (
+ "n_jobs",
+ "pre_dispatch",
+ "memory",
+ "_path",
+ "_dir",
+ "nthread",
+ "callbacks",
+)
def _eval_search_params(params_builder):
@@ -100,33 +120,29 @@
imblearn.under_sampling.CondensedNearestNeighbour(
random_state=0, n_jobs=N_JOBS
),
- imblearn.under_sampling.EditedNearestNeighbours(
- random_state=0, n_jobs=N_JOBS
- ),
- imblearn.under_sampling.RepeatedEditedNearestNeighbours(
- random_state=0, n_jobs=N_JOBS
- ),
- imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS),
+ imblearn.under_sampling.EditedNearestNeighbours(n_jobs=N_JOBS),
+ imblearn.under_sampling.RepeatedEditedNearestNeighbours(n_jobs=N_JOBS),
+ imblearn.under_sampling.AllKNN(n_jobs=N_JOBS),
imblearn.under_sampling.InstanceHardnessThreshold(
random_state=0, n_jobs=N_JOBS
),
- imblearn.under_sampling.NearMiss(random_state=0, n_jobs=N_JOBS),
- imblearn.under_sampling.NeighbourhoodCleaningRule(
- random_state=0, n_jobs=N_JOBS
- ),
+ imblearn.under_sampling.NearMiss(n_jobs=N_JOBS),
+ imblearn.under_sampling.NeighbourhoodCleaningRule(n_jobs=N_JOBS),
imblearn.under_sampling.OneSidedSelection(
random_state=0, n_jobs=N_JOBS
),
imblearn.under_sampling.RandomUnderSampler(random_state=0),
- imblearn.under_sampling.TomekLinks(random_state=0, n_jobs=N_JOBS),
+ imblearn.under_sampling.TomekLinks(n_jobs=N_JOBS),
imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS),
+ imblearn.over_sampling.BorderlineSMOTE(random_state=0, n_jobs=N_JOBS),
+ imblearn.over_sampling.KMeansSMOTE(random_state=0, n_jobs=N_JOBS),
imblearn.over_sampling.RandomOverSampler(random_state=0),
imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS),
- imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
- imblearn.over_sampling.BorderlineSMOTE(random_state=0, n_jobs=N_JOBS),
+ imblearn.over_sampling.SMOTEN(random_state=0, n_jobs=N_JOBS),
imblearn.over_sampling.SMOTENC(
categorical_features=[], random_state=0, n_jobs=N_JOBS
),
+ imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
imblearn.combine.SMOTEENN(random_state=0),
imblearn.combine.SMOTETomek(random_state=0),
)
@@ -288,7 +304,12 @@
loaded_df[df_key] = infile2
y = read_columns(
- infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
+ infile2,
+ c=c,
+ c_option=column_option,
+ sep="\t",
+ header=header,
+ parse_dates=True,
)
if len(y.shape) == 2 and y.shape[1] == 1:
y = y.ravel()
@@ -416,24 +437,19 @@
print(repr(warning.message))
scorer_ = searcher.scorer_
- if isinstance(scorer_, collections.Mapping):
- is_multimetric = True
- else:
- is_multimetric = False
best_estimator_ = getattr(searcher, "best_estimator_")
# TODO Solve deep learning models in pipeline
if best_estimator_.__class__.__name__ == "KerasGBatchClassifier":
test_score = best_estimator_.evaluate(
- X_test, scorer=scorer_, is_multimetric=is_multimetric
+ X_test,
+ scorer=scorer_,
)
else:
- test_score = _score(
- best_estimator_, X_test, y_test, scorer_, is_multimetric=is_multimetric
- )
+ test_score = _score(best_estimator_, X_test, y_test, scorer_)
- if not is_multimetric:
+ if not isinstance(scorer_, dict):
test_score = {primary_scoring: test_score}
for key, value in test_score.items():
test_score[key] = [value]
@@ -443,6 +459,34 @@
return searcher
+def _set_memory(estimator, memory):
+ """set memeory cache
+
+ Parameters
+ ----------
+ estimator : python object
+ memory : joblib.Memory object
+
+ Returns
+ -------
+ estimator : estimator object after setting new attributes
+ """
+ if isinstance(estimator, IRAPSClassifier):
+ estimator.set_params(memory=memory)
+ return estimator
+
+ estimator_params = estimator.get_params()
+
+ new_params = {}
+ for k in estimator_params.keys():
+ if k.endswith("irapsclassifier__memory"):
+ new_params[k] = memory
+
+ estimator.set_params(**new_params)
+
+ return estimator
+
+
def main(
inputs,
infile_estimator,
@@ -450,7 +494,6 @@
infile2,
outfile_result,
outfile_object=None,
- outfile_weights=None,
groups=None,
ref_seq=None,
intervals=None,
@@ -461,10 +504,10 @@
Parameter
---------
inputs : str
- File path to galaxy tool parameter
+ File path to galaxy tool parameter.
infile_estimator : str
- File path to estimator
+ File path to estimator.
infile1 : str
File path to dataset containing features
@@ -478,9 +521,6 @@
outfile_object : str, optional
File path to save searchCV object
- outfile_weights : str, optional
- File path to save model weights
-
groups : str
File path to dataset containing groups labels
@@ -505,18 +545,38 @@
params = json.load(param_handler)
# Override the refit parameter
- params["search_schemes"]["options"]["refit"] = (
- True if params["save"] != "nope" else False
+ params["options"]["refit"] = (
+ True
+ if (
+ params["save"] != "nope"
+ or params["outer_split"]["split_mode"] == "nested_cv"
+ )
+ else False
)
- with open(infile_estimator, "rb") as estimator_handler:
- estimator = load_model(estimator_handler)
+ estimator = load_model_from_h5(infile_estimator)
+
+ estimator = clean_params(estimator)
+
+ if estimator.__class__.__name__ == "KerasGBatchClassifier":
+ _fit_and_score = try_get_attr(
+ "galaxy_ml.model_validations",
+ "_fit_and_score",
+ )
- optimizer = params["search_schemes"]["selected_search_scheme"]
- optimizer = getattr(model_selection, optimizer)
+ setattr(_search, "_fit_and_score", _fit_and_score)
+ setattr(_validation, "_fit_and_score", _fit_and_score)
+
+ search_algos_and_options = params["search_algos"]
+ optimizer = search_algos_and_options.pop("selected_search_algo")
+ if optimizer == "skopt.BayesSearchCV":
+ optimizer = BayesSearchCV
+ else:
+ optimizer = getattr(model_selection, optimizer)
# handle gridsearchcv options
- options = params["search_schemes"]["options"]
+ options = params["options"]
+ options.update(search_algos_and_options)
if groups:
header = (
@@ -553,38 +613,36 @@
groups = groups.ravel()
options["cv_selector"]["groups_selector"] = groups
- splitter, groups = get_cv(options.pop("cv_selector"))
+ cv_selector = options.pop("cv_selector")
+ if Version(galaxy_ml_version) < Version("0.8.3"):
+ cv_selector.pop("n_stratification_bins", None)
+ splitter, groups = get_cv(cv_selector)
options["cv"] = splitter
primary_scoring = options["scoring"]["primary_scoring"]
- # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
- # Check if secondary_scoring is specified
- secondary_scoring = options["scoring"].get("secondary_scoring", None)
- if secondary_scoring is not None:
- # If secondary_scoring is specified, convert the list into comman separated string
- options["scoring"]["secondary_scoring"] = ",".join(
- options["scoring"]["secondary_scoring"]
+ options["scoring"] = get_scoring(options["scoring"])
+ # TODO make BayesSearchCV support multiple scoring
+ if optimizer == "skopt.BayesSearchCV" and isinstance(options["scoring"], dict):
+ options["scoring"] = options["scoring"][primary_scoring]
+ warnings.warn(
+ "BayesSearchCV doesn't support multiple "
+ "scorings! Primary scoring is used."
)
- options["scoring"] = get_scoring(options["scoring"])
if options["error_score"]:
options["error_score"] = "raise"
else:
- options["error_score"] = np.nan
+ options["error_score"] = np.NaN
if options["refit"] and isinstance(options["scoring"], dict):
options["refit"] = primary_scoring
if "pre_dispatch" in options and options["pre_dispatch"] == "":
options["pre_dispatch"] = None
- params_builder = params["search_schemes"]["search_params_builder"]
+ params_builder = params["search_params_builder"]
param_grid = _eval_search_params(params_builder)
- estimator = clean_params(estimator)
-
# save the SearchCV object without fit
if params["save"] == "save_no_fit":
searcher = optimizer(estimator, param_grid, **options)
- print(searcher)
- with open(outfile_object, "wb") as output_handler:
- pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL)
+ dump_model_to_h5(searcher, outfile_object)
return 0
# read inputs and loads new attributes, like paths
@@ -600,37 +658,36 @@
fasta_path=fasta_path,
)
+ label_encoder = LabelEncoder()
+ if get_main_estimator(estimator).__class__.__name__ == "XGBClassifier":
+ y = label_encoder.fit_transform(y)
+
# cache iraps_core fits could increase search speed significantly
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
- main_est = get_main_estimator(estimator)
- if main_est.__class__.__name__ == "IRAPSClassifier":
- main_est.set_params(memory=memory)
+ estimator = _set_memory(estimator, memory)
searcher = optimizer(estimator, param_grid, **options)
split_mode = params["outer_split"].pop("split_mode")
+ # Nested CV
if split_mode == "nested_cv":
- # make sure refit is choosen
- # this could be True for sklearn models, but not the case for
- # deep learning models
- if not options["refit"] and not all(
- hasattr(estimator, attr) for attr in ("config", "model_type")
- ):
- warnings.warn("Refit is change to `True` for nested validation!")
- setattr(searcher, "refit", True)
-
- outer_cv, _ = get_cv(params["outer_split"]["cv_selector"])
+ cv_selector = params["outer_split"]["cv_selector"]
+ if Version(galaxy_ml_version) < Version("0.8.3"):
+ cv_selector.pop("n_stratification_bins", None)
+ outer_cv, _ = get_cv(cv_selector)
# nested CV, outer cv using cross_validate
if options["error_score"] == "raise":
rval = cross_validate(
searcher,
X,
y,
+ groups=groups,
scoring=options["scoring"],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options["verbose"],
+ fit_params={"groups": groups},
return_estimator=(params["save"] == "save_estimator"),
error_score=options["error_score"],
return_train_score=True,
@@ -643,10 +700,12 @@
searcher,
X,
y,
+ groups=groups,
scoring=options["scoring"],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options["verbose"],
+ fit_params={"groups": groups},
return_estimator=(params["save"] == "save_estimator"),
error_score=options["error_score"],
return_train_score=True,
@@ -676,8 +735,6 @@
cv_results_.to_csv(target_path, sep="\t", header=True, index=False)
except Exception as e:
print(e)
- finally:
- del os
keys = list(rval.keys())
for k in keys:
@@ -689,6 +746,9 @@
rval = pd.DataFrame(rval)
rval = rval[sorted(rval.columns)]
rval.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False)
+
+ return 0
+
# deprecate train test split mode
"""searcher = _do_train_test_split_val(
searcher, X, y, params,
@@ -696,7 +756,6 @@
error_score=options['error_score'],
groups=groups,
outfile=outfile_result)"""
- return 0
# no outer split
else:
@@ -732,24 +791,7 @@
)
return
- # clean prams
- best_estimator_ = clean_params(best_estimator_)
-
- main_est = get_main_estimator(best_estimator_)
-
- if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
- if outfile_weights:
- main_est.save_weights(outfile_weights)
- del main_est.model_
- del main_est.fit_params
- del main_est.model_class_
- del main_est.validation_data
- if getattr(main_est, "data_generator_", None):
- del main_est.data_generator_
-
- with open(outfile_object, "wb") as output_handler:
- print("Best estimator is saved: %s " % repr(best_estimator_))
- pickle.dump(best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL)
+ dump_model_to_h5(best_estimator_, outfile_object)
if __name__ == "__main__":
@@ -760,7 +802,6 @@
aparser.add_argument("-y", "--infile2", dest="infile2")
aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
- aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
aparser.add_argument("-g", "--groups", dest="groups")
aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
aparser.add_argument("-b", "--intervals", dest="intervals")
@@ -768,17 +809,4 @@
aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
args = aparser.parse_args()
- main(
- args.inputs,
- args.infile_estimator,
- args.infile1,
- args.infile2,
- args.outfile_result,
- outfile_object=args.outfile_object,
- outfile_weights=args.outfile_weights,
- groups=args.groups,
- ref_seq=args.ref_seq,
- intervals=args.intervals,
- targets=args.targets,
- fasta_path=args.fasta_path,
- )
+ main(**vars(args))
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 simple_model_fit.py
--- a/simple_model_fit.py Thu Aug 11 09:49:51 2022 +0000
+++ b/simple_model_fit.py Wed Aug 09 13:23:40 2023 +0000
@@ -1,9 +1,9 @@
import argparse
import json
-import pickle
import pandas as pd
-from galaxy_ml.utils import load_model, read_columns
+from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
+from galaxy_ml.utils import read_columns
from scipy.io import mmread
from sklearn.pipeline import Pipeline
@@ -148,9 +148,9 @@
params = json.load(param_handler)
# load model
- with open(infile_estimator, "rb") as est_handler:
- estimator = load_model(est_handler)
- estimator = clean_params(estimator, n_jobs=N_JOBS)
+ estimator = load_model_from_h5(infile_estimator)
+
+ estimator = clean_params(estimator)
X_train, y_train = _get_X_y(params, infile1, infile2)
@@ -170,8 +170,7 @@
if getattr(main_est, "data_generator_", None):
del main_est.data_generator_
- with open(out_object, "wb") as output_handler:
- pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
+ dump_model_to_h5(estimator, out_object)
if __name__ == "__main__":
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 stacking_ensembles.py
--- a/stacking_ensembles.py Thu Aug 11 09:49:51 2022 +0000
+++ b/stacking_ensembles.py Wed Aug 09 13:23:40 2023 +0000
@@ -1,22 +1,22 @@
import argparse
import ast
import json
-import pickle
import sys
import warnings
+from distutils.version import LooseVersion as Version
import mlxtend.classifier
import mlxtend.regressor
-import pandas as pd
-from galaxy_ml.utils import (get_cv, get_estimator, get_search_params,
- load_model)
+from galaxy_ml import __version__ as galaxy_ml_version
+from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
+from galaxy_ml.utils import get_cv, get_estimator
warnings.filterwarnings("ignore")
N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
-def main(inputs_path, output_obj, base_paths=None, meta_path=None, outfile_params=None):
+def main(inputs_path, output_obj, base_paths=None, meta_path=None):
"""
Parameter
---------
@@ -31,9 +31,6 @@
meta_path : str
File path
-
- outfile_params : str
- File path for params output
"""
with open(inputs_path, "r") as param_handler:
params = json.load(param_handler)
@@ -43,8 +40,7 @@
base_estimators = []
for idx, base_file in enumerate(base_paths.split(",")):
if base_file and base_file != "None":
- with open(base_file, "rb") as handler:
- model = load_model(handler)
+ model = load_model_from_h5(base_file)
else:
estimator_json = params["base_est_builder"][idx]["estimator_selector"]
model = get_estimator(estimator_json)
@@ -59,8 +55,7 @@
# get meta estimator, if applicable
if estimator_type.startswith("mlxtend"):
if meta_path:
- with open(meta_path, "rb") as f:
- meta_estimator = load_model(f)
+ meta_estimator = load_model_from_h5(meta_path)
else:
estimator_json = params["algo_selection"]["meta_estimator"][
"estimator_selector"
@@ -71,7 +66,9 @@
cv_selector = options.pop("cv_selector", None)
if cv_selector:
- splitter, _groups = get_cv(cv_selector)
+ if Version(galaxy_ml_version) < Version("0.8.3"):
+ cv_selector.pop("n_stratification_bins", None)
+ splitter, groups = get_cv(cv_selector)
options["cv"] = splitter
# set n_jobs
options["n_jobs"] = N_JOBS
@@ -104,13 +101,7 @@
for base_est in base_estimators:
print(base_est)
- with open(output_obj, "wb") as out_handler:
- pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL)
-
- if params["get_params"] and outfile_params:
- results = get_search_params(ensemble_estimator)
- df = pd.DataFrame(results, columns=["", "Parameter", "Value"])
- df.to_csv(outfile_params, sep="\t", index=False)
+ dump_model_to_h5(ensemble_estimator, output_obj)
if __name__ == "__main__":
@@ -119,13 +110,6 @@
aparser.add_argument("-m", "--meta", dest="meta")
aparser.add_argument("-i", "--inputs", dest="inputs")
aparser.add_argument("-o", "--outfile", dest="outfile")
- aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
args = aparser.parse_args()
- main(
- args.inputs,
- args.outfile,
- base_paths=args.bases,
- meta_path=args.meta,
- outfile_params=args.outfile_params,
- )
+ main(args.inputs, args.outfile, base_paths=args.bases, meta_path=args.meta)
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/GridSearchCV01.h5mlm
Binary file test-data/GridSearchCV01.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/LinearRegression01.h5mlm
Binary file test-data/LinearRegression01.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/RFE.h5mlm
Binary file test-data/RFE.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/RandomForestClassifier.h5mlm
Binary file test-data/RandomForestClassifier.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/RandomForestRegressor01.h5mlm
Binary file test-data/RandomForestRegressor01.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/StackingCVRegressor01.h5mlm
Binary file test-data/StackingCVRegressor01.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/StackingRegressor02.h5mlm
Binary file test-data/StackingRegressor02.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/StackingVoting03.h5mlm
Binary file test-data/StackingVoting03.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/XGBRegressor01.h5mlm
Binary file test-data/XGBRegressor01.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/abc_model01
Binary file test-data/abc_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/abc_result01
--- a/test-data/abc_result01 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/abc_result01 Wed Aug 09 13:23:40 2023 +0000
@@ -1,6 +1,6 @@
0 1 2 3 predicted
-3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1
-0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0
-2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1
-1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+3.68258022948 2.82110345641 -3.990140724 -1.9523364774 1
+0.015942057224 -0.711958594347 0.125502976978 -0.972218263337 0
+2.08690768825 0.929399321468 -2.12924084484 -1.99714022188 1
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/abr_model01
Binary file test-data/abr_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/abr_result01
--- a/test-data/abr_result01 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/abr_result01 Wed Aug 09 13:23:40 2023 +0000
@@ -1,6 +1,6 @@
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/auc.txt
--- a/test-data/auc.txt Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/auc.txt Wed Aug 09 13:23:40 2023 +0000
@@ -1,2 +1,2 @@
auc :
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/best_estimator_.h5mlm
Binary file test-data/best_estimator_.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/best_params_.txt
--- a/test-data/best_params_.txt Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/best_params_.txt Wed Aug 09 13:23:40 2023 +0000
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-{'estimator__n_estimators': 100}
\ No newline at end of file
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/best_score_.tabular
--- a/test-data/best_score_.tabular Thu Aug 11 09:49:51 2022 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/classifier_y.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result01.txt
--- a/test-data/cluster_result01.txt Thu Aug 11 09:49:51 2022 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result02.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result03.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result04.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result05.txt
--- a/test-data/cluster_result05.txt Thu Aug 11 09:49:51 2022 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result06.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result07.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result08.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result09.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result10.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result11.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result12.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result13.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result14.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result15.txt
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result16.txt
--- a/test-data/cluster_result16.txt Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/cluster_result16.txt Wed Aug 09 13:23:40 2023 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/cluster_result20.txt
--- a/test-data/cluster_result20.txt Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/cluster_result20.txt Wed Aug 09 13:23:40 2023 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/feature_importances_.tabular
--- a/test-data/feature_importances_.tabular Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/feature_importances_.tabular Wed Aug 09 13:23:40 2023 +0000
@@ -1,11 +1,18 @@
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/feature_selection_result01
--- a/test-data/feature_selection_result01 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/feature_selection_result01 Wed Aug 09 13:23:40 2023 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/feature_selection_result08
--- a/test-data/feature_selection_result08 Thu Aug 11 09:49:51 2022 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/feature_selection_result09
--- a/test-data/feature_selection_result09 Thu Aug 11 09:49:51 2022 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/feature_selection_result12
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/final_estimator.h5mlm
Binary file test-data/final_estimator.h5mlm has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/fitted_model_eval01.tabular
--- a/test-data/fitted_model_eval01.tabular Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/fitted_model_eval01.tabular Wed Aug 09 13:23:40 2023 +0000
@@ -1,2 +1,2 @@
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/gbc_model01
Binary file test-data/gbc_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/gbc_result01
--- a/test-data/gbc_result01 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/gbc_result01 Wed Aug 09 13:23:40 2023 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/gbr_model01
Binary file test-data/gbr_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/get_params.tabular
--- a/test-data/get_params.tabular Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/get_params.tabular Wed Aug 09 13:23:40 2023 +0000
@@ -1,6 +1,7 @@
Parameter Value
@ copy_X copy_X: True
@ fit_intercept fit_intercept: True
-* n_jobs n_jobs: 1
-@ normalize normalize: False
+* n_jobs n_jobs: None
+@ normalize normalize: 'deprecated'
+@ positive positive: False
Note: @, params eligible for search in searchcv tool.
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model01
Binary file test-data/glm_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model02
Binary file test-data/glm_model02 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model03
Binary file test-data/glm_model03 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model04
Binary file test-data/glm_model04 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model05
Binary file test-data/glm_model05 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model06
Binary file test-data/glm_model06 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model07
Binary file test-data/glm_model07 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_model08
Binary file test-data/glm_model08 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result01
--- a/test-data/glm_result01 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result01 Wed Aug 09 13:23:40 2023 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result02
--- a/test-data/glm_result02 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result02 Wed Aug 09 13:23:40 2023 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result03
--- a/test-data/glm_result03 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result03 Wed Aug 09 13:23:40 2023 +0000
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diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result04
--- a/test-data/glm_result04 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result04 Wed Aug 09 13:23:40 2023 +0000
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+-47.4101632272 -0.638416457964 -0.732777468453 -0.864026104978 -1.06109770116 0.012682414140452014
+61.7128046302 -1.09994800577 -0.739679672932 0.585657963012 1.48906827536 0.18698422341553234
+-206.998295124 0.130238853011 0.70574123041 1.33206565264 -1.33220923738 -1.659936090430246
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result05
--- a/test-data/glm_result05 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result05 Wed Aug 09 13:23:40 2023 +0000
@@ -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 1
-1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+3.68258022948 2.82110345641 -3.990140724 -1.9523364774 1
+0.015942057224 -0.711958594347 0.125502976978 -0.972218263337 0
+2.08690768825 0.929399321468 -2.12924084484 -1.99714022188 1
+1.41321052084 0.523750660422 -1.4210539291 -1.49298569451 1
+0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result06
--- a/test-data/glm_result06 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result06 Wed Aug 09 13:23:40 2023 +0000
@@ -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 1
-1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0
+0.015942057224 -0.711958594347 0.125502976978 -0.972218263337 0
+2.08690768825 0.929399321468 -2.12924084484 -1.99714022188 0
+1.41321052084 0.523750660422 -1.4210539291 -1.49298569451 0
+0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result07
--- a/test-data/glm_result07 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result07 Wed Aug 09 13:23:40 2023 +0000
@@ -1,5 +1,5 @@
-86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.6093152833692663
-91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5963828164943974
--47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.07927429227257948
-61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.2621440442022235
--206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.7330414645145749
+86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.6093152833692668
+91.2021798817 -0.621522971207 1.11914889596 0.390012184498 1.28956938152 0.5963828164943976
+-47.4101632272 -0.638416457964 -0.732777468453 -0.864026104978 -1.06109770116 -0.07927429227258004
+61.7128046302 -1.09994800577 -0.739679672932 0.585657963012 1.48906827536 0.26214404420222365
+-206.998295124 0.130238853011 0.70574123041 1.33206565264 -1.33220923738 -1.7330414645145753
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/glm_result08
--- a/test-data/glm_result08 Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/glm_result08 Wed Aug 09 13:23:40 2023 +0000
@@ -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
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+3.68258022948 2.82110345641 -3.990140724 -1.9523364774 1
+0.015942057224 -0.711958594347 0.125502976978 -0.972218263337 0
+2.08690768825 0.929399321468 -2.12924084484 -1.99714022188 0
+1.41321052084 0.523750660422 -1.4210539291 -1.49298569451 0
+0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/jaccard_similarity_score.txt
--- a/test-data/jaccard_similarity_score.txt Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/jaccard_similarity_score.txt Wed Aug 09 13:23:40 2023 +0000
@@ -1,2 +1,2 @@
-jaccard_similarity_score :
-0.8461538461538461
+jaccard_score :
+0.7538461538461538
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras01.json
--- a/test-data/keras01.json Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/keras01.json Wed Aug 09 13:23:40 2023 +0000
@@ -1,12 +1,25 @@
{
"class_name": "Sequential",
"config": {
- "name": "sequential_1",
+ "name": "sequential",
"layers": [
{
+ "class_name": "InputLayer",
+ "config": {
+ "batch_input_shape": [
+ null,
+ 784
+ ],
+ "dtype": "float32",
+ "sparse": false,
+ "ragged": false,
+ "name": "dense_input"
+ }
+ },
+ {
"class_name": "Dense",
"config": {
- "name": "dense_1",
+ "name": "dense",
"trainable": true,
"batch_input_shape": [
null,
@@ -17,11 +30,43 @@
"activation": "linear",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"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",
+ "trainable": true,
+ "dtype": "float32",
+ "activation": "relu"
+ }
+ },
+ {
+ "class_name": "Dense",
+ "config": {
+ "name": "dense_1",
+ "trainable": true,
+ "dtype": "float32",
+ "units": 10,
+ "activation": "linear",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
"seed": null
}
},
@@ -42,49 +87,11 @@
"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",
+ "keras_version": "2.10.0",
"backend": "tensorflow"
}
\ No newline at end of file
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras02.json
--- a/test-data/keras02.json Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/keras02.json Wed Aug 09 13:23:40 2023 +0000
@@ -1,10 +1,9 @@
{
- "class_name": "Model",
+ "class_name": "Functional",
"config": {
- "name": "model_1",
+ "name": "model",
"layers": [
{
- "name": "main_input",
"class_name": "InputLayer",
"config": {
"batch_input_shape": [
@@ -13,15 +12,16 @@
],
"dtype": "int32",
"sparse": false,
+ "ragged": false,
"name": "main_input"
},
+ "name": "main_input",
"inbound_nodes": []
},
{
- "name": "embedding_1",
"class_name": "Embedding",
"config": {
- "name": "embedding_1",
+ "name": "embedding",
"trainable": true,
"batch_input_shape": [
null,
@@ -44,6 +44,7 @@
"mask_zero": false,
"input_length": 100
},
+ "name": "embedding",
"inbound_nodes": [
[
[
@@ -56,10 +57,9 @@
]
},
{
- "name": "lstm_1",
"class_name": "LSTM",
"config": {
- "name": "lstm_1",
+ "name": "lstm",
"trainable": true,
"dtype": "float32",
"return_sequences": false,
@@ -67,29 +67,30 @@
"go_backwards": false,
"stateful": false,
"unroll": false,
+ "time_major": false,
"units": 32,
"activation": "tanh",
"recurrent_activation": "sigmoid",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"config": {
- "scale": 1.0,
- "mode": "fan_avg",
- "distribution": "uniform",
"seed": null
- }
+ },
+ "shared_object_id": 3
},
"recurrent_initializer": {
"class_name": "Orthogonal",
"config": {
"gain": 1.0,
"seed": null
- }
+ },
+ "shared_object_id": 4
},
"bias_initializer": {
"class_name": "Zeros",
- "config": {}
+ "config": {},
+ "shared_object_id": 5
},
"unit_forget_bias": true,
"kernel_regularizer": null,
@@ -103,10 +104,11 @@
"recurrent_dropout": 0.0,
"implementation": 2
},
+ "name": "lstm",
"inbound_nodes": [
[
[
- "embedding_1",
+ "embedding",
0,
0,
{}
@@ -115,21 +117,17 @@
]
},
{
- "name": "dense_1",
"class_name": "Dense",
"config": {
- "name": "dense_1",
+ "name": "dense",
"trainable": true,
"dtype": "float32",
"units": 1,
"activation": "sigmoid",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"config": {
- "scale": 1.0,
- "mode": "fan_avg",
- "distribution": "uniform",
"seed": null
}
},
@@ -143,10 +141,11 @@
"kernel_constraint": null,
"bias_constraint": null
},
+ "name": "dense",
"inbound_nodes": [
[
[
- "lstm_1",
+ "lstm",
0,
0,
{}
@@ -155,7 +154,6 @@
]
},
{
- "name": "aux_input",
"class_name": "InputLayer",
"config": {
"batch_input_shape": [
@@ -164,23 +162,25 @@
],
"dtype": "float32",
"sparse": false,
+ "ragged": false,
"name": "aux_input"
},
+ "name": "aux_input",
"inbound_nodes": []
},
{
- "name": "concatenate_1",
"class_name": "Concatenate",
"config": {
- "name": "concatenate_1",
+ "name": "concatenate",
"trainable": true,
"dtype": "float32",
"axis": -1
},
+ "name": "concatenate",
"inbound_nodes": [
[
[
- "dense_1",
+ "dense",
0,
0,
{}
@@ -195,21 +195,17 @@
]
},
{
- "name": "dense_2",
"class_name": "Dense",
"config": {
- "name": "dense_2",
+ "name": "dense_1",
"trainable": true,
"dtype": "float32",
"units": 64,
"activation": "relu",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"config": {
- "scale": 1.0,
- "mode": "fan_avg",
- "distribution": "uniform",
"seed": null
}
},
@@ -223,10 +219,11 @@
"kernel_constraint": null,
"bias_constraint": null
},
+ "name": "dense_1",
"inbound_nodes": [
[
[
- "concatenate_1",
+ "concatenate",
0,
0,
{}
@@ -235,21 +232,17 @@
]
},
{
- "name": "dense_3",
"class_name": "Dense",
"config": {
- "name": "dense_3",
+ "name": "dense_2",
"trainable": true,
"dtype": "float32",
"units": 64,
"activation": "relu",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"config": {
- "scale": 1.0,
- "mode": "fan_avg",
- "distribution": "uniform",
"seed": null
}
},
@@ -263,10 +256,11 @@
"kernel_constraint": null,
"bias_constraint": null
},
+ "name": "dense_2",
"inbound_nodes": [
[
[
- "dense_2",
+ "dense_1",
0,
0,
{}
@@ -275,21 +269,17 @@
]
},
{
- "name": "dense_4",
"class_name": "Dense",
"config": {
- "name": "dense_4",
+ "name": "dense_3",
"trainable": true,
"dtype": "float32",
"units": 64,
"activation": "relu",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"config": {
- "scale": 1.0,
- "mode": "fan_avg",
- "distribution": "uniform",
"seed": null
}
},
@@ -303,10 +293,11 @@
"kernel_constraint": null,
"bias_constraint": null
},
+ "name": "dense_3",
"inbound_nodes": [
[
[
- "dense_3",
+ "dense_2",
0,
0,
{}
@@ -315,21 +306,17 @@
]
},
{
- "name": "dense_5",
"class_name": "Dense",
"config": {
- "name": "dense_5",
+ "name": "dense_4",
"trainable": true,
"dtype": "float32",
"units": 1,
"activation": "sigmoid",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"config": {
- "scale": 1.0,
- "mode": "fan_avg",
- "distribution": "uniform",
"seed": null
}
},
@@ -343,10 +330,11 @@
"kernel_constraint": null,
"bias_constraint": null
},
+ "name": "dense_4",
"inbound_nodes": [
[
[
- "dense_4",
+ "dense_3",
0,
0,
{}
@@ -369,17 +357,17 @@
],
"output_layers": [
[
- "dense_1",
+ "dense",
0,
0
],
[
- "dense_5",
+ "dense_4",
0,
0
]
]
},
- "keras_version": "2.3.1",
+ "keras_version": "2.10.0",
"backend": "tensorflow"
}
\ No newline at end of file
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras03.json
--- a/test-data/keras03.json Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/keras03.json Wed Aug 09 13:23:40 2023 +0000
@@ -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": 100, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": 0}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.1, "noise_shape": null, "seed": 0}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": 0}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
+{
+ "class_name": "Sequential",
+ "config": {
+ "name": "sequential",
+ "layers": [
+ {
+ "class_name": "InputLayer",
+ "config": {
+ "batch_input_shape": [
+ null,
+ 17
+ ],
+ "dtype": "float32",
+ "sparse": false,
+ "ragged": false,
+ "name": "dense_input"
+ }
+ },
+ {
+ "class_name": "Dense",
+ "config": {
+ "name": "dense",
+ "trainable": true,
+ "batch_input_shape": [
+ null,
+ 17
+ ],
+ "dtype": "float32",
+ "units": 100,
+ "activation": "linear",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
+ "seed": null
+ }
+ },
+ "bias_initializer": {
+ "class_name": "Zeros",
+ "config": {}
+ },
+ "kernel_regularizer": null,
+ "bias_regularizer": null,
+ "activity_regularizer": null,
+ "kernel_constraint": null,
+ "bias_constraint": null
+ }
+ },
+ {
+ "class_name": "Dropout",
+ "config": {
+ "name": "dropout",
+ "trainable": true,
+ "dtype": "float32",
+ "rate": 0.1,
+ "noise_shape": null,
+ "seed": null
+ }
+ },
+ {
+ "class_name": "Dense",
+ "config": {
+ "name": "dense_1",
+ "trainable": true,
+ "dtype": "float32",
+ "units": 1,
+ "activation": "linear",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
+ "seed": null
+ }
+ },
+ "bias_initializer": {
+ "class_name": "Zeros",
+ "config": {}
+ },
+ "kernel_regularizer": null,
+ "bias_regularizer": null,
+ "activity_regularizer": null,
+ "kernel_constraint": null,
+ "bias_constraint": null
+ }
+ }
+ ]
+ },
+ "keras_version": "2.10.0",
+ "backend": "tensorflow"
+}
\ No newline at end of file
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras04.json
--- a/test-data/keras04.json Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/keras04.json Wed Aug 09 13:23:40 2023 +0000
@@ -1,12 +1,25 @@
{
"class_name": "Sequential",
"config": {
- "name": "sequential_1",
+ "name": "sequential",
"layers": [
{
+ "class_name": "InputLayer",
+ "config": {
+ "batch_input_shape": [
+ null,
+ 17
+ ],
+ "dtype": "float32",
+ "sparse": false,
+ "ragged": false,
+ "name": "dense_input"
+ }
+ },
+ {
"class_name": "Dense",
"config": {
- "name": "dense_1",
+ "name": "dense",
"trainable": true,
"batch_input_shape": [
null,
@@ -17,11 +30,43 @@
"activation": "linear",
"use_bias": true,
"kernel_initializer": {
- "class_name": "VarianceScaling",
+ "class_name": "GlorotUniform",
"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",
+ "trainable": true,
+ "dtype": "float32",
+ "activation": "linear"
+ }
+ },
+ {
+ "class_name": "Dense",
+ "config": {
+ "name": "dense_1",
+ "trainable": true,
+ "dtype": "float32",
+ "units": 1,
+ "activation": "linear",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
"seed": null
}
},
@@ -44,47 +89,9 @@
"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",
+ "keras_version": "2.10.0",
"backend": "tensorflow"
}
\ No newline at end of file
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras05.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras05.json Wed Aug 09 13:23:40 2023 +0000
@@ -0,0 +1,243 @@
+{
+ "class_name": "Sequential",
+ "config": {
+ "name": "sequential",
+ "layers": [
+ {
+ "class_name": "InputLayer",
+ "config": {
+ "batch_input_shape": [
+ null,
+ 32,
+ 32,
+ 3
+ ],
+ "dtype": "float32",
+ "sparse": false,
+ "ragged": false,
+ "name": "conv2d_input"
+ }
+ },
+ {
+ "class_name": "Conv2D",
+ "config": {
+ "name": "conv2d",
+ "trainable": true,
+ "batch_input_shape": [
+ null,
+ 32,
+ 32,
+ 3
+ ],
+ "dtype": "float32",
+ "filters": 32,
+ "kernel_size": [
+ 3,
+ 3
+ ],
+ "strides": [
+ 1,
+ 1
+ ],
+ "padding": "same",
+ "data_format": "channels_last",
+ "dilation_rate": [
+ 1,
+ 1
+ ],
+ "groups": 1,
+ "activation": "relu",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
+ "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": "MaxPooling2D",
+ "config": {
+ "name": "max_pooling2d",
+ "trainable": true,
+ "dtype": "float32",
+ "pool_size": [
+ 2,
+ 2
+ ],
+ "padding": "valid",
+ "strides": [
+ 2,
+ 2
+ ],
+ "data_format": "channels_last"
+ }
+ },
+ {
+ "class_name": "Dropout",
+ "config": {
+ "name": "dropout",
+ "trainable": true,
+ "dtype": "float32",
+ "rate": 0.25,
+ "noise_shape": null,
+ "seed": null
+ }
+ },
+ {
+ "class_name": "Conv2D",
+ "config": {
+ "name": "conv2d_1",
+ "trainable": true,
+ "dtype": "float32",
+ "filters": 64,
+ "kernel_size": [
+ 3,
+ 3
+ ],
+ "strides": [
+ 1,
+ 1
+ ],
+ "padding": "same",
+ "data_format": "channels_last",
+ "dilation_rate": [
+ 1,
+ 1
+ ],
+ "groups": 1,
+ "activation": "relu",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
+ "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": "MaxPooling2D",
+ "config": {
+ "name": "max_pooling2d_1",
+ "trainable": true,
+ "dtype": "float32",
+ "pool_size": [
+ 2,
+ 2
+ ],
+ "padding": "valid",
+ "strides": [
+ 2,
+ 2
+ ],
+ "data_format": "channels_last"
+ }
+ },
+ {
+ "class_name": "Dropout",
+ "config": {
+ "name": "dropout_1",
+ "trainable": true,
+ "dtype": "float32",
+ "rate": 0.25,
+ "noise_shape": null,
+ "seed": null
+ }
+ },
+ {
+ "class_name": "Flatten",
+ "config": {
+ "name": "flatten",
+ "trainable": true,
+ "dtype": "float32",
+ "data_format": "channels_last"
+ }
+ },
+ {
+ "class_name": "Dense",
+ "config": {
+ "name": "dense",
+ "trainable": true,
+ "dtype": "float32",
+ "units": 512,
+ "activation": "relu",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
+ "seed": null
+ }
+ },
+ "bias_initializer": {
+ "class_name": "Zeros",
+ "config": {}
+ },
+ "kernel_regularizer": null,
+ "bias_regularizer": null,
+ "activity_regularizer": null,
+ "kernel_constraint": null,
+ "bias_constraint": null
+ }
+ },
+ {
+ "class_name": "Dropout",
+ "config": {
+ "name": "dropout_2",
+ "trainable": true,
+ "dtype": "float32",
+ "rate": 0.5,
+ "noise_shape": null,
+ "seed": null
+ }
+ },
+ {
+ "class_name": "Dense",
+ "config": {
+ "name": "dense_1",
+ "trainable": true,
+ "dtype": "float32",
+ "units": 10,
+ "activation": "softmax",
+ "use_bias": true,
+ "kernel_initializer": {
+ "class_name": "GlorotUniform",
+ "config": {
+ "seed": null
+ }
+ },
+ "bias_initializer": {
+ "class_name": "Zeros",
+ "config": {}
+ },
+ "kernel_regularizer": null,
+ "bias_regularizer": null,
+ "activity_regularizer": null,
+ "kernel_constraint": null,
+ "bias_constraint": null
+ }
+ }
+ ]
+ },
+ "keras_version": "2.10.0",
+ "backend": "tensorflow"
+}
\ No newline at end of file
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_batch_model01
Binary file test-data/keras_batch_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_batch_model02
Binary file test-data/keras_batch_model02 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_batch_model03
Binary file test-data/keras_batch_model03 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_batch_model04
Binary file test-data/keras_batch_model04 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_batch_model05
Binary file test-data/keras_batch_model05 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_model01
Binary file test-data/keras_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_model02
Binary file test-data/keras_model02 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/keras_model04
Binary file test-data/keras_model04 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/lda_model01
Binary file test-data/lda_model01 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/lda_model02
Binary file test-data/lda_model02 has changed
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/lda_prediction_result01.tabular
--- a/test-data/lda_prediction_result01.tabular Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/lda_prediction_result01.tabular Wed Aug 09 13:23:40 2023 +0000
@@ -1,5 +1,6 @@
-3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0
-0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0
-2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0
-1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+0 1 2 3 0
+3.68258022948 2.82110345641 -3.990140724 -1.9523364774 1
+0.015942057224 -0.711958594347 0.125502976978 -0.972218263337 0
+2.08690768825 0.929399321468 -2.12924084484 -1.99714022188 0
+1.41321052084 0.523750660422 -1.4210539291 -1.49298569451 0
+0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/lda_prediction_result02.tabular
--- a/test-data/lda_prediction_result02.tabular Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/lda_prediction_result02.tabular Wed Aug 09 13:23:40 2023 +0000
@@ -1,5 +1,6 @@
-3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0
-0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0
-2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0
-1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+0 1 2 3 0
+3.68258022948 2.82110345641 -3.990140724 -1.9523364774 1
+0.015942057224 -0.711958594347 0.125502976978 -0.972218263337 0
+2.08690768825 0.929399321468 -2.12924084484 -1.99714022188 0
+1.41321052084 0.523750660422 -1.4210539291 -1.49298569451 0
+0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/lgb_class_model.txt
--- a/test-data/lgb_class_model.txt Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/lgb_class_model.txt Wed Aug 09 13:23:40 2023 +0000
@@ -7,7 +7,7 @@
objective=binary sigmoid:1
feature_names=Column_0 Column_1 Column_2 Column_3
feature_infos=none none none none
-tree_sizes=228
+tree_sizes=239
Tree=0
num_leaves=1
@@ -18,18 +18,19 @@
decision_type=
left_child=
right_child=
-leaf_value=-0.40546510810816427
+leaf_value=-0.2876820724517809
leaf_weight=
leaf_count=
internal_value=
internal_weight=
internal_count=
+is_linear=0
shrinkage=1
end of trees
-feature importances:
+feature_importances:
parameters:
[boosting: gbdt]
@@ -39,10 +40,14 @@
[device_type: cpu]
[data: ]
[valid: ]
-[num_iterations: 100]
+[num_iterations: 10000]
[learning_rate: 0.02]
[num_leaves: 32]
-[num_threads: 0]
+[num_threads: 1]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
[max_depth: 8]
[min_data_in_leaf: 20]
[min_sum_hessian_in_leaf: 39]
@@ -54,11 +59,14 @@
[feature_fraction: 0.9]
[feature_fraction_bynode: 1]
[feature_fraction_seed: 26500]
+[extra_trees: 0]
+[extra_seed: 15724]
[early_stopping_round: 0]
[first_metric_only: 0]
[max_delta_step: 0]
[lambda_l1: 0.04]
[lambda_l2: 0.07]
+[linear_lambda: 0]
[min_gain_to_split: 0.02]
[drop_rate: 0.1]
[max_drop: 50]
@@ -75,51 +83,41 @@
[max_cat_to_onehot: 4]
[top_k: 20]
[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
[feature_contri: ]
[forcedsplits_filename: ]
-[forcedbins_filename: ]
[refit_decay_rate: 0.9]
[cegb_tradeoff: 1]
[cegb_penalty_split: 0]
[cegb_penalty_feature_lazy: ]
[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
[verbosity: -1]
+[saved_feature_importance_type: 0]
+[linear_tree: 0]
[max_bin: 255]
[max_bin_by_feature: ]
[min_data_in_bin: 3]
[bin_construct_sample_cnt: 200000]
-[histogram_pool_size: -1]
[data_random_seed: 41]
-[output_model: LightGBM_model.txt]
-[snapshot_freq: -1]
-[input_model: ]
-[output_result: LightGBM_predict_result.txt]
-[initscore_filename: ]
-[valid_data_initscores: ]
-[pre_partition: 0]
+[is_enable_sparse: 1]
[enable_bundle: 1]
-[max_conflict_rate: 0]
-[is_enable_sparse: 1]
-[sparse_threshold: 0.8]
[use_missing: 1]
[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
[two_round: 0]
-[save_binary: 0]
[header: 0]
[label_column: ]
[weight_column: ]
[group_column: ]
[ignore_column: ]
[categorical_feature: ]
-[predict_raw_score: 0]
-[predict_leaf_index: 0]
-[predict_contrib: 0]
-[num_iteration_predict: -1]
-[pred_early_stop: 0]
-[pred_early_stop_freq: 10]
-[pred_early_stop_margin: 10]
-[convert_model_language: ]
-[convert_model: gbdt_prediction.cpp]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[objective_seed: 19169]
[num_class: 1]
[is_unbalance: 0]
[scale_pos_weight: 1]
@@ -130,13 +128,12 @@
[fair_c: 1]
[poisson_max_delta_step: 0.7]
[tweedie_variance_power: 1.5]
-[max_position: 20]
-[lambdamart_norm: 1]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
[label_gain: ]
-[metric_freq: 1]
-[is_provide_training_metric: 0]
[eval_at: ]
[multi_error_top_k: 1]
+[auc_mu_weights: ]
[num_machines: 1]
[local_listen_port: 12400]
[time_out: 120]
@@ -145,6 +142,7 @@
[gpu_platform_id: -1]
[gpu_device_id: -1]
[gpu_use_dp: 0]
+[num_gpu: 1]
end of parameters
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/lgb_regr_model.txt
--- a/test-data/lgb_regr_model.txt Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/lgb_regr_model.txt Wed Aug 09 13:23:40 2023 +0000
@@ -7,180034 +7,1937 @@
objective=regression
feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16
feature_infos=none [1:12] [1:31] [35:89] [35:117] [45.100000000000001:77.400000000000006] [41:77] [46:81] [44:79] [28:95] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1]
-tree_sizes=518 530 532 530 531 532 530 534 533 535 535 532 532 535 534 530 534 533 532 533 533 537 534 456 531 456 534 458 458 536 538 532 536 613 538 532 536 458 533 536 613 533 537 536 533 537 534 537 537 536 458 532 535 612 538 537 614 617 615 615 538 539 615 616 617 539 616 536 615 537 615 539 540 618 619 617 538 540 458 539 457 459 461 460 538 616 613 612 614 459 617 542 618 461 541 615 617 540 619 542 618 622 541 618 622 543 541 541 618 619 538 619 617 462 542 620 618 459 618 541 544 457 460 623 619 461 545 459 625 461 624 624 544 620 623 543 622 623 544 544 622 621 544 545 464 544 462 624 621 543 623 463 543 622 622 545 462 543 624 464 622 464 624 616 463 464 624 464 624 543 541 623 464 617 541 545 625 541 620 542 541 541 542 542 518 543 539 547 542 547 541 546 542 543 541 540 626 543 544 543 545 548 541 545 546 542 543 545 547 548 546 544 546 543 542 548 544 547 541 543 546 547 540 544 545 542 545 626 543 539 463 544 544 542 544 545 545 544 539 546 628 544 545 547 543 545 547 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+internal_count=261 183 143
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shrinkage=0.02
end of trees
-feature importances:
-Column_9=8322
-Column_2=6585
-Column_5=5272
-Column_4=4915
-Column_6=4114
-Column_3=3831
-Column_1=3507
-Column_7=2717
-Column_8=2340
+feature_importances:
+Column_4=145
+Column_5=52
+Column_7=32
+Column_6=31
+Column_8=19
+Column_2=9
+Column_3=9
+Column_9=3
parameters:
[boosting: gbdt]
[objective: regression]
-[metric: ]
+[metric: l2]
[tree_learner: serial]
[device_type: cpu]
[data: ]
[valid: ]
[num_iterations: 100]
[learning_rate: 0.02]
-[num_leaves: 32]
-[num_threads: 0]
-[max_depth: 8]
+[num_leaves: 4]
+[num_threads: 1]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 4]
[min_data_in_leaf: 20]
[min_sum_hessian_in_leaf: 39]
[bagging_fraction: 0.9]
@@ -180045,11 +1948,14 @@
[feature_fraction: 0.9]
[feature_fraction_bynode: 1]
[feature_fraction_seed: 26500]
+[extra_trees: 0]
+[extra_seed: 15724]
[early_stopping_round: 0]
[first_metric_only: 0]
[max_delta_step: 0]
[lambda_l1: 0.04]
[lambda_l2: 0.07]
+[linear_lambda: 0]
[min_gain_to_split: 0.02]
[drop_rate: 0.1]
[max_drop: 50]
@@ -180066,51 +1972,41 @@
[max_cat_to_onehot: 4]
[top_k: 20]
[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
[feature_contri: ]
[forcedsplits_filename: ]
-[forcedbins_filename: ]
[refit_decay_rate: 0.9]
[cegb_tradeoff: 1]
[cegb_penalty_split: 0]
[cegb_penalty_feature_lazy: ]
[cegb_penalty_feature_coupled: ]
-[verbosity: -1]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 0]
+[saved_feature_importance_type: 0]
+[linear_tree: 0]
[max_bin: 255]
[max_bin_by_feature: ]
[min_data_in_bin: 3]
[bin_construct_sample_cnt: 200000]
-[histogram_pool_size: -1]
[data_random_seed: 41]
-[output_model: LightGBM_model.txt]
-[snapshot_freq: -1]
-[input_model: ]
-[output_result: LightGBM_predict_result.txt]
-[initscore_filename: ]
-[valid_data_initscores: ]
-[pre_partition: 0]
+[is_enable_sparse: 1]
[enable_bundle: 1]
-[max_conflict_rate: 0]
-[is_enable_sparse: 1]
-[sparse_threshold: 0.8]
[use_missing: 1]
[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
[two_round: 0]
-[save_binary: 0]
[header: 0]
[label_column: ]
[weight_column: ]
[group_column: ]
[ignore_column: ]
[categorical_feature: ]
-[predict_raw_score: 0]
-[predict_leaf_index: 0]
-[predict_contrib: 0]
-[num_iteration_predict: -1]
-[pred_early_stop: 0]
-[pred_early_stop_freq: 10]
-[pred_early_stop_margin: 10]
-[convert_model_language: ]
-[convert_model: gbdt_prediction.cpp]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[objective_seed: 19169]
[num_class: 1]
[is_unbalance: 0]
[scale_pos_weight: 1]
@@ -180121,13 +2017,12 @@
[fair_c: 1]
[poisson_max_delta_step: 0.7]
[tweedie_variance_power: 1.5]
-[max_position: 20]
-[lambdamart_norm: 1]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
[label_gain: ]
-[metric_freq: 1]
-[is_provide_training_metric: 0]
[eval_at: ]
[multi_error_top_k: 1]
+[auc_mu_weights: ]
[num_machines: 1]
[local_listen_port: 12400]
[time_out: 120]
@@ -180136,6 +2031,7 @@
[gpu_platform_id: -1]
[gpu_device_id: -1]
[gpu_use_dp: 0]
+[num_gpu: 1]
end of parameters
diff -r 8a794e6d3388 -r 6eb4e7fb0f91 test-data/ml_vis01.html
--- a/test-data/ml_vis01.html Thu Aug 11 09:49:51 2022 +0000
+++ b/test-data/ml_vis01.html Wed Aug 09 13:23:40 2023 +0000
@@ -1,31 +1,67 @@
-