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models.py
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import numpy as np
from catboost import CatBoostClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import RidgeClassifier
from sklearn.naive_bayes import BernoulliNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
random_state = 0
# Modelos Shallow (Supervised)
shallow_models = {
# STOCHASTIC
"NB": BernoulliNB(),
"SVC": SVC(random_state=random_state, probability=False),
"RGC": RidgeClassifier(),
# TREE-BASED
"DT": DecisionTreeClassifier(random_state=random_state),
# EMBED-BASED
"RF": RandomForestClassifier(random_state=random_state),
"ABC": AdaBoostClassifier(random_state=random_state),
"CB": CatBoostClassifier(random_state=random_state, verbose=False, logging_level=None),
"XGB": XGBClassifier(random_state=random_state, metric='multiclass', eval_metric='mlogloss', verbosity=0),
"XT": ExtraTreesClassifier(random_state=random_state),
# NEURALNETWORK BASED
"MLP": MLPClassifier(random_state=random_state),
# DISTANCE BASED
"KNN": KNeighborsClassifier()
}
# IN general in sklearn Parallel support, n_jobs = -1 n_jobsint, default=None The number of jobs to run in parallel.
# fit, predict, decision_path and apply are all parallelized over the trees. None means 1 unless in a
# joblib.parallel_backend context. -1 means using all processors.
models_gpu = {
# STOCHASTIC
# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB
# No GPU support in sklearn
# No Parallel support
"NB": BernoulliNB(),
# https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
# No GPU support in sklearn
# No Parallel support
"SVC": SVC(random_state=random_state),
# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifierCV.html#sklearn.linear_model.RidgeClassifierCV
# No GPU support in sklearn
# No Parallel support
"RGC": RidgeClassifier(),
# TREE-BASED
# https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn-tree-decisiontreeclassifier
# No GPU support in sklearn
# No Parallel support
"DT": DecisionTreeClassifier(random_state=random_state),
# EMBED-BASED
# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn-ensemble-randomforestclassifier
# No GPU support in sklearn
# Parallel support, n_jobs = -1
"RF": RandomForestClassifier(random_state=random_state, n_jobs=-1),
# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn-ensemble-adaboostclassifier
# No GPU support in sklearn
# No Parallel support
# "ABC": AdaBoostClassifier(random_state=random_state),
# https://catboost.ai/en/docs/features/training-on-gpu
# task_type="GPU", devices=0:get_gpu_device_count()
"CB": CatBoostClassifier(random_state=random_state, verbose=False, logging_level=None, task_type="GPU"),
# https://xgboost.readthedocs.io/en/stable/python/python_api.html
# tree_method='gpu_hist',
"XGB": XGBClassifier(random_state=random_state, metric='multiclass', eval_metric='mlogloss', verbosity=0,
tree_method='gpu_hist'),
# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn-ensemble-extratreesclassifier
# No GPU support in sklearn
# Parallel support, n_jobs = -1
"XT": ExtraTreesClassifier(random_state=random_state, n_jobs=-1),
# NEURALNETWORK BASED
# https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier
# No GPU support in sklearn
# No Parallel support
"MLP": MLPClassifier(random_state=random_state),
# DISTANCE BASED
# https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn-neighbors-kneighborsclassifier
# No GPU support in sklearn
# Parallel support, n_jobs = -1
"KNN": KNeighborsClassifier(n_jobs=-1)
}
label_models = list(shallow_models.keys())
param_grid = {
"NB": {
'alpha': [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000],
'fit_prior': [True, False]
},
"SVC": {
'C': [0.1, 1.0, 10.0, 100.0],
'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001]
},
"RGC": {
'alpha': [0.01, 0.1, 1.0, 10.0, 100.0],
'solver': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']
},
"DT": {
'criterion': ['gini', 'entropy'],
'max_depth': [None, 10, 50, 100],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4, 8, 16]
},
"RF": {
'n_estimators': [int(x) for x in np.linspace(start=200, stop=1000, num=3)],
'max_features': ['sqrt', 'log2'],
'max_depth': [int(x) for x in np.linspace(10, 30, num=2)] + [None],
'min_samples_split': [2, 5],
'min_samples_leaf': [1, 2, 4],
'bootstrap': [True, False]
},
"ABC": {
'n_estimators': [10, 50, 100, 500],
'learning_rate': [0.0001, 0.001, 0.01, 0.1, 1.0]
},
"CB": {
'learning_rate': [0.03, 0.1],
'depth': [4, 6, 10],
'l2_leaf_reg': [1, 3, 5, 7, 9]
},
"XGB": {
'max_depth': [3, 5, 7],
'learning_rate': [0.1, 0.01, 0.001],
'subsample': [0.5, 0.7, 1]
},
"XT": {
"random_state": [0, 1, 2, 3, 4],
"n_estimators": [320, 340, 360, 380],
"max_depth": [30, 32, 34, 38]
},
"MLP": {
'hidden_layer_sizes': [(50,), (100,), (50, 50), (100, 100)],
'activation': ['logistic', 'tanh', 'relu'],
'alpha': 10.0 ** -np.arange(1, 5),
'max_iter': [1000]
},
"KNN": {
'n_neighbors': [5, 10, 15, 20],
'weights': ['uniform', 'distance'],
'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],
'p': [1, 2],
'leaf_size': [5, 10],
}
}