# HG changeset patch
# User bgruening
# Date 1691583760 0
# Node ID 108141350edbd0f9a73f270ee4a3ecf9d09a7dbd
# Parent 23df4520fd583032b66a8957b7e91459ae230f7a
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
diff -r 23df4520fd58 -r 108141350edb association_rules.py
--- a/association_rules.py Thu Aug 11 09:09:12 2022 +0000
+++ b/association_rules.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb fitted_model_eval.py
--- a/fitted_model_eval.py Thu Aug 11 09:09:12 2022 +0000
+++ b/fitted_model_eval.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb keras_deep_learning.py
--- a/keras_deep_learning.py Thu Aug 11 09:09:12 2022 +0000
+++ b/keras_deep_learning.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb keras_macros.xml
--- a/keras_macros.xml Thu Aug 11 09:09:12 2022 +0000
+++ b/keras_macros.xml Wed Aug 09 12:22:40 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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diff -r 23df4520fd58 -r 108141350edb keras_train_and_eval.py
--- a/keras_train_and_eval.py Thu Aug 11 09:09:12 2022 +0000
+++ b/keras_train_and_eval.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb label_encoder.py
--- a/label_encoder.py Thu Aug 11 09:09:12 2022 +0000
+++ b/label_encoder.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb label_encoder.xml
--- a/label_encoder.xml Thu Aug 11 09:09:12 2022 +0000
+++ b/label_encoder.xml Wed Aug 09 12:22:40 2023 +0000
@@ -1,4 +1,4 @@
-
+
Encode target labels with value between 0 and n_classes-1
main_macros.xml
diff -r 23df4520fd58 -r 108141350edb main_macros.xml
--- a/main_macros.xml Thu Aug 11 09:09:12 2022 +0000
+++ b/main_macros.xml Wed Aug 09 12:22:40 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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+
+
-
-
-
- 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 23df4520fd58 -r 108141350edb ml_visualization_ex.py
--- a/ml_visualization_ex.py Thu Aug 11 09:09:12 2022 +0000
+++ b/ml_visualization_ex.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb model_prediction.py
--- a/model_prediction.py Thu Aug 11 09:09:12 2022 +0000
+++ b/model_prediction.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb pdb70_cs219.ffdata
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/pdb70_cs219.ffdata Wed Aug 09 12:22:40 2023 +0000
@@ -0,0 +1,189 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Galaxy
+ | Europe
+ |
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ You are not allowed to access this dataset
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff -r 23df4520fd58 -r 108141350edb search_model_validation.py
--- a/search_model_validation.py Thu Aug 11 09:09:12 2022 +0000
+++ b/search_model_validation.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb simple_model_fit.py
--- a/simple_model_fit.py Thu Aug 11 09:09:12 2022 +0000
+++ b/simple_model_fit.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb stacking_ensembles.py
--- a/stacking_ensembles.py Thu Aug 11 09:09:12 2022 +0000
+++ b/stacking_ensembles.py Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/GridSearchCV01.h5mlm
Binary file test-data/GridSearchCV01.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/LinearRegression01.h5mlm
Binary file test-data/LinearRegression01.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/RFE.h5mlm
Binary file test-data/RFE.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/RandomForestClassifier.h5mlm
Binary file test-data/RandomForestClassifier.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/RandomForestRegressor01.h5mlm
Binary file test-data/RandomForestRegressor01.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/StackingCVRegressor01.h5mlm
Binary file test-data/StackingCVRegressor01.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/StackingRegressor02.h5mlm
Binary file test-data/StackingRegressor02.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/StackingVoting03.h5mlm
Binary file test-data/StackingVoting03.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/XGBRegressor01.h5mlm
Binary file test-data/XGBRegressor01.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/abc_model01
Binary file test-data/abc_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/abc_result01
--- a/test-data/abc_result01 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/abc_result01 Wed Aug 09 12:22:40 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
+1.41321052084 0.523750660422 -1.4210539291 -1.49298569451 1
+0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 23df4520fd58 -r 108141350edb test-data/abr_model01
Binary file test-data/abr_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/abr_result01
--- a/test-data/abr_result01 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/abr_result01 Wed Aug 09 12:22:40 2023 +0000
@@ -1,6 +1,6 @@
0 1 2 3 4 predicted
-86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.323842059244
-91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 1.1503117056799999
--47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.7191695359690001
-61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 1.1503117056799999
--206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -0.7191695359690001
+86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.94315222831
+91.2021798817 -0.621522971207 1.11914889596 0.390012184498 1.28956938152 1.0812585465566666
+-47.4101632272 -0.638416457964 -0.732777468453 -0.864026104978 -1.06109770116 -0.719169535969
+61.7128046302 -1.09994800577 -0.739679672932 0.585657963012 1.48906827536 1.15031170568
+-206.998295124 0.130238853011 0.70574123041 1.33206565264 -1.33220923738 -0.719169535969
diff -r 23df4520fd58 -r 108141350edb test-data/auc.txt
--- a/test-data/auc.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/auc.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,2 +1,2 @@
auc :
-2.5
+3.0
diff -r 23df4520fd58 -r 108141350edb test-data/best_estimator_.h5mlm
Binary file test-data/best_estimator_.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/best_params_.txt
--- a/test-data/best_params_.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/best_params_.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,1 +1,1 @@
-{'estimator__n_estimators': 100}
\ No newline at end of file
+{}
\ No newline at end of file
diff -r 23df4520fd58 -r 108141350edb test-data/best_score_.tabular
--- a/test-data/best_score_.tabular Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/best_score_.tabular Wed Aug 09 12:22:40 2023 +0000
@@ -1,2 +1,2 @@
best_score_
-0.7976348550293088
+0.8065123295049499
diff -r 23df4520fd58 -r 108141350edb test-data/classifier_y.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/classifier_y.tabular Wed Aug 09 12:22:40 2023 +0000
@@ -0,0 +1,262 @@
+label
+high
+intermediate
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+intermediate
diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result01.txt
--- a/test-data/cluster_result01.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result01.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 0
0 44 64 -76 0
0 51 48 -73 0
0 58 65 -49 0
diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result02.txt
--- a/test-data/cluster_result02.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result02.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,48 +1,49 @@
-0 44 64 -76 3
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result03.txt
--- a/test-data/cluster_result03.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result03.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 -1
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result04.txt
--- a/test-data/cluster_result04.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result04.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 1
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result05.txt
--- a/test-data/cluster_result05.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result05.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 0
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result06.txt
--- a/test-data/cluster_result06.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result06.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 0
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result07.txt
--- a/test-data/cluster_result07.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result07.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 0
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result08.txt
--- a/test-data/cluster_result08.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result08.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 0
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result09.txt
--- a/test-data/cluster_result09.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result09.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 0
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result10.txt
--- a/test-data/cluster_result10.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result10.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 3
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result11.txt
--- a/test-data/cluster_result11.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result11.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,3 +1,4 @@
+0 58 56 -67 2
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result12.txt
--- a/test-data/cluster_result12.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/cluster_result12.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,48 +1,49 @@
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result13.txt
--- a/test-data/cluster_result13.txt Thu Aug 11 09:09:12 2022 +0000
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result14.txt
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result15.txt
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result16.txt
--- a/test-data/cluster_result16.txt Thu Aug 11 09:09:12 2022 +0000
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diff -r 23df4520fd58 -r 108141350edb test-data/cluster_result20.txt
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diff -r 23df4520fd58 -r 108141350edb test-data/feature_importances_.tabular
--- a/test-data/feature_importances_.tabular Thu Aug 11 09:09:12 2022 +0000
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diff -r 23df4520fd58 -r 108141350edb test-data/feature_selection_result01
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diff -r 23df4520fd58 -r 108141350edb test-data/feature_selection_result08
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diff -r 23df4520fd58 -r 108141350edb test-data/feature_selection_result09
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diff -r 23df4520fd58 -r 108141350edb test-data/feature_selection_result12
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diff -r 23df4520fd58 -r 108141350edb test-data/final_estimator.h5mlm
Binary file test-data/final_estimator.h5mlm has changed
diff -r 23df4520fd58 -r 108141350edb test-data/fitted_model_eval01.tabular
--- a/test-data/fitted_model_eval01.tabular Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/fitted_model_eval01.tabular Wed Aug 09 12:22:40 2023 +0000
@@ -1,2 +1,2 @@
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diff -r 23df4520fd58 -r 108141350edb test-data/gbc_model01
Binary file test-data/gbc_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/gbc_result01
--- a/test-data/gbc_result01 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/gbc_result01 Wed Aug 09 12:22:40 2023 +0000
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-2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1
-1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+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 23df4520fd58 -r 108141350edb test-data/gbr_model01
Binary file test-data/gbr_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/get_params.tabular
--- a/test-data/get_params.tabular Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/get_params.tabular Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/glm_model01
Binary file test-data/glm_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_model02
Binary file test-data/glm_model02 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_model03
Binary file test-data/glm_model03 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_model04
Binary file test-data/glm_model04 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_model05
Binary file test-data/glm_model05 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_model06
Binary file test-data/glm_model06 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_model07
Binary file test-data/glm_model07 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_model08
Binary file test-data/glm_model08 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/glm_result01
--- a/test-data/glm_result01 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result01 Wed Aug 09 12:22:40 2023 +0000
@@ -1,5 +1,5 @@
-86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 20479602419382.055
-91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 21460309408632.004
--47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -11245419999724.842
-61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 14574106078789.26
--206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -48782519807586.32
+86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 20479602419372.582
+91.2021798817 -0.621522971207 1.11914889596 0.390012184498 1.28956938152 21460309408622.086
+-47.4101632272 -0.638416457964 -0.732777468453 -0.864026104978 -1.06109770116 -11245419999719.686
+61.7128046302 -1.09994800577 -0.739679672932 0.585657963012 1.48906827536 14574106078782.537
+-206.998295124 0.130238853011 0.70574123041 1.33206565264 -1.33220923738 -48782519807563.79
diff -r 23df4520fd58 -r 108141350edb test-data/glm_result02
--- a/test-data/glm_result02 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result02 Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/glm_result03
--- a/test-data/glm_result03 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result03 Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/glm_result04
--- a/test-data/glm_result04 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result04 Wed Aug 09 12:22:40 2023 +0000
@@ -1,5 +1,5 @@
-86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.5282637592226301
-91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5180352211818147
--47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 0.012682414140451959
-61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.1869842234155321
--206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.6599360904302456
+86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.5282637592226304
+91.2021798817 -0.621522971207 1.11914889596 0.390012184498 1.28956938152 0.5180352211818147
+-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 23df4520fd58 -r 108141350edb test-data/glm_result05
--- a/test-data/glm_result05 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result05 Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/glm_result06
--- a/test-data/glm_result06 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result06 Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/glm_result07
--- a/test-data/glm_result07 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result07 Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/glm_result08
--- a/test-data/glm_result08 Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/glm_result08 Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/jaccard_similarity_score.txt
--- a/test-data/jaccard_similarity_score.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/jaccard_similarity_score.txt Wed Aug 09 12:22:40 2023 +0000
@@ -1,2 +1,2 @@
-jaccard_similarity_score :
-0.8461538461538461
+jaccard_score :
+0.7538461538461538
diff -r 23df4520fd58 -r 108141350edb test-data/keras01.json
--- a/test-data/keras01.json Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/keras01.json Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/keras02.json
--- a/test-data/keras02.json Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/keras02.json Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/keras03.json
--- a/test-data/keras03.json Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/keras03.json Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/keras04.json
--- a/test-data/keras04.json Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/keras04.json Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/keras05.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/keras05.json Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/keras_batch_model01
Binary file test-data/keras_batch_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/keras_batch_model02
Binary file test-data/keras_batch_model02 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/keras_batch_model03
Binary file test-data/keras_batch_model03 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/keras_batch_model04
Binary file test-data/keras_batch_model04 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/keras_batch_model05
Binary file test-data/keras_batch_model05 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/keras_model01
Binary file test-data/keras_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/keras_model02
Binary file test-data/keras_model02 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/keras_model04
Binary file test-data/keras_model04 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/lda_model01
Binary file test-data/lda_model01 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/lda_model02
Binary file test-data/lda_model02 has changed
diff -r 23df4520fd58 -r 108141350edb test-data/lda_prediction_result01.tabular
--- a/test-data/lda_prediction_result01.tabular Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/lda_prediction_result01.tabular Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/lda_prediction_result02.tabular
--- a/test-data/lda_prediction_result02.tabular Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/lda_prediction_result02.tabular Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/lgb_class_model.txt
--- a/test-data/lgb_class_model.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/lgb_class_model.txt Wed Aug 09 12:22:40 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 23df4520fd58 -r 108141350edb test-data/lgb_regr_model.txt
--- a/test-data/lgb_regr_model.txt Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/lgb_regr_model.txt Wed Aug 09 12:22:40 2023 +0000
@@ -7,180034 +7,1937 @@
objective=regression
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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 23df4520fd58 -r 108141350edb test-data/ml_vis01.html
--- a/test-data/ml_vis01.html Thu Aug 11 09:09:12 2022 +0000
+++ b/test-data/ml_vis01.html Wed Aug 09 12:22:40 2023 +0000
@@ -1,31 +1,67 @@
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