2. ML Experiments

import matplotlib.pyplot as plt
from SeqMetrics import ClassificationMetrics
from ai4water.experiments import MLClassificationExperiments

from utils import SAVE
from utils import version_info, return_train_test, set_rcParams

version_info()

python 3.7.17 (default, Jun 25 2023, 21:20:48)
[GCC 11.3.0]
os posix
ai4water 1.07
lightgbm 4.0.0
easy_mpl 0.21.4
SeqMetrics 1.3.4
tensorflow 1.15.0
tensorflow.python.keras.api._v1.keras 2.2.4-tf
numpy 1.19.5
pandas 1.3.5
matplotlib 3.5.3
h5py 2.10.0
sklearn 1.0.2
optuna 3.2.0
skopt 0.9.0
seaborn 0.12.2
shap 0.41.0

set_rcParams()

def f1_weighted(t,p)->float:
    return ClassificationMetrics(t, p).f1_score(average="weighted")

def f1_micro(t,p)->float:
    return ClassificationMetrics(t, p).f1_score(average="micro")

def precision_weighted(t,p)->float:
    return ClassificationMetrics(t, p).precision(average="weighted")

def precision_micro(t,p)->float:
    return ClassificationMetrics(t, p).precision(average="micro")

def recall_weighted(t,p)->float:
    return ClassificationMetrics(t, p).recall(average="weighted")

def recall_micro(t,p)->float:
    return ClassificationMetrics(t, p).recall(average="micro")

WQ and Antibiotics

scenario = 'no_genes'

CTX-M

target = 'CTX-M'

TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.
running  BernoulliNB model
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
running  DummyClassifier model
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running  ExtraTreeClassifier model
running  ExtraTreesClassifier model
running  GradientBoostingClassifier model
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divide by zero encountered in true_divide
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running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000070 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
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divide by zero encountered in true_divide
invalid value encountered in true_divide
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running  LogisticRegression model
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
running  MLPClassifier model
Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
running  NearestCentroid model
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divide by zero encountered in true_divide
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running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
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divide by zero encountered in true_divide
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running  Perceptron model
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divide by zero encountered in true_divide
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running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
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running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

OXA48

target = 'OXA48'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
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running  DummyClassifier model
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divide by zero encountered in true_divide
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running  ExtraTreeClassifier model
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divide by zero encountered in true_divide
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running  ExtraTreesClassifier model
divide by zero encountered in true_divide
running  GradientBoostingClassifier model
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divide by zero encountered in true_divide
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running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000046 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  LogisticRegression model
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
running  MLPClassifier model
Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
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divide by zero encountered in true_divide
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running  Perceptron model
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divide by zero encountered in true_divide
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running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

TEM

target = 'TEM'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
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running  ExtraTreesClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000047 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  LogisticRegression model
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
running  MLPClassifier model
Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  Perceptron model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  SVC model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

MCR-1

target = 'MCR-1'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
running  ExtraTreesClassifier model
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000047 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  LogisticRegression model
lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
running  MLPClassifier model
Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  Perceptron model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8),)
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

Only WQ

scenario = 'only_wq'

CTX-M

target = 'CTX-M'

TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
running  ExtraTreesClassifier model
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  LogisticRegression model
running  MLPClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  Perceptron model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  SVC model

_ = comparisons.compare_errors('f1_score',
                               x=TestX,
                               y=TestY.values,
                               colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
                                figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

OXA48

target = 'OXA48'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreesClassifier model
divide by zero encountered in true_divide
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000038 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  LogisticRegression model
running  MLPClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  Perceptron model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide

TEM

target = 'TEM'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
running  ExtraTreesClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000037 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  LogisticRegression model
running  MLPClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  Perceptron model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  SVC model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

MCR-1

target = 'MCR-1'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  CalibratedClassifierCV model
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
Liblinear failed to converge, increase the number of iterations.
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
running  ExtraTreesClassifier model
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000039 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  LogisticRegression model
running  MLPClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  Perceptron model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide

Only Antibiotics

scenario = 'only_antibiotics'

CTX-M

target = 'CTX-M'

TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
running  ExtraTreesClassifier model
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000053 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
running  LogisticRegression model
running  MLPClassifier model
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
running  Perceptron model
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
running  SVC model

_ = comparisons.compare_errors('f1_score',
                               x=TestX,
                               y=TestY.values,
                               colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
                                figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

OXA48

target = 'OXA48'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreesClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000041 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
running  LogisticRegression model
running  MLPClassifier model
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  Perceptron model
divide by zero encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

TEM

target = 'TEM'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreesClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000042 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
running  LogisticRegression model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  MLPClassifier model
Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
running  Perceptron model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RidgeClassifierCV was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LogisticRegression was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but ExtraTreeClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names

MCR-1

target = 'MCR-1'
TrainX, TrainY, TestX, TestY, inputs = return_train_test(target, scenario)

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

comparisons = MLClassificationExperiments(
    input_features=inputs,
    output_features=target,
    monitor=['f1_score', 'accuracy',
             'precision', 'recall',
             f1_micro, f1_weighted,
             precision_micro, precision_weighted,
             recall_micro, recall_weighted,
             ],
    verbosity=0,
    show=False
)

comparisons.fit(x=TrainX.values,
                y=TrainY.values,
                exclude=['LinearDiscriminantAnalysis',
                         'BaggingClassifier',
                         'HistGradientBoostingClassifier',
                         'DecisionTreeClassifier',
                         'GaussianProcessClassifier']
                )

running  AdaBoostClassifier model
running  BernoulliNB model
running  CalibratedClassifierCV model
running  DummyClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  ExtraTreeClassifier model
running  ExtraTreesClassifier model
running  GradientBoostingClassifier model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  KNeighborsClassifier model
running  LGBMClassifier model
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000041 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
running  LabelPropagation model
running  LabelSpreading model
running  LinearSVC model
Liblinear failed to converge, increase the number of iterations.
running  LogisticRegression model
running  MLPClassifier model
Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
running  NearestCentroid model
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
divide by zero encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
invalid value encountered in true_divide
running  NuSVC model
Solver terminated early (max_iter=100).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
running  PassiveAggressiveClassifier model
running  Perceptron model
running  QuadraticDiscriminantAnalysis model
running  RandomForestClassifier model
running  RidgeClassifier model
running  RidgeClassifierCV model
running  SGDClassifier model
running  SVC model

_ = comparisons.compare_errors(
    'f1_score',
    x=TestX,
    y=TestY.values,
    colors=['salmon', 'cadetblue'],
    label_bars=True,
    bar_label_kws={"color": 'black', 'label_type':'center'},
    figsize=(10, 8))
plt.tight_layout()
if SAVE:
    plt.savefig(f"results/figures/exp_{target}_{scenario}", bbox_inches="tight", dpi=600)
plt.show()

invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but KNeighborsClassifier was fitted without feature names
X has feature names, but SGDClassifier was fitted without feature names
X has feature names, but RidgeClassifierCV was fitted without feature names
X has feature names, but LogisticRegression was fitted without feature names
X has feature names, but PassiveAggressiveClassifier was fitted without feature names
X has feature names, but NuSVC was fitted without feature names
X has feature names, but MLPClassifier was fitted without feature names
X has feature names, but AdaBoostClassifier was fitted without feature names
X has feature names, but RidgeClassifier was fitted without feature names
X has feature names, but ExtraTreeClassifier was fitted without feature names
[LightGBM] [Warning] bagging_freq is set=1, subsample_freq=0 will be ignored. Current value: bagging_freq=1
[LightGBM] [Warning] bagging_fraction is set=0.5, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5
X has feature names, but ExtraTreesClassifier was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but LinearSVC was fitted without feature names
X has feature names, but QuadraticDiscriminantAnalysis was fitted without feature names
X has feature names, but SVC was fitted without feature names
X has feature names, but Perceptron was fitted without feature names
X has feature names, but NearestCentroid was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but RandomForestClassifier was fitted without feature names
X has feature names, but GradientBoostingClassifier was fitted without feature names
invalid value encountered in true_divide
invalid value encountered in true_divide
X has feature names, but LabelSpreading was fitted without feature names
X has feature names, but LabelPropagation was fitted without feature names
X has feature names, but BernoulliNB was fitted without feature names