def xgboost_func():
method = "XGBClassifier"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + "_lyrics" + ".txt"
# setting the model parameters
clf = method_func(max_depth=None,
learning_rate=0.1,
n_estimators=100,
silent=True,
objective='multi:softmax',
nthread=5,
gamma=0,
min_child_weight=1,
max_delta_step=0,
subsample=1,
colsample_bytree=1,
colsample_bylevel=1,
reg_alpha=0,
reg_lambda=1,
scale_pos_weight=1,
base_score=0.5,
seed=0,
missing=None)
# use a full grid search over following parameters
param_grid = {"max_depth": [100, 400]}
run_model_util.run_model_lyrics(clf, param_grid, method, file_name)
def grad_boosting_func():
method = "GradientBoostingClassifier"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + "_lyrics" + ".txt"
# setting the model parameters
clf = method_func(loss='deviance',
learning_rate=0.1,
n_estimators=100,
subsample=1.0,
criterion='friedman_mse',
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_depth=3,
min_impurity_split=1e-07,
init=None,
random_state=None,
max_features=None,
verbose=0,
max_leaf_nodes=None,
warm_start=False,
presort='auto')
# use a full grid search over following parameters
param_grid = {"max_depth": [50, 500], "n_estimators": [50, 200]}
run_model_util.run_model_lyrics(clf, param_grid, method, file_name)
def random_forest_func():
method = "RandomForestClassifier"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + "_lyrics" + ".txt"
# setting the model parameters
clf = method_func(n_estimators=100,
criterion='gini',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features='auto',
max_leaf_nodes=None,
min_impurity_split=1e-07,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None)
# use a full grid search over all parameters
param_grid = {"max_depth": [50, 500], "max_features": [10, 50]}
run_model_util.run_model_lyrics(clf, param_grid, method, file_name)
def logistic_reg_func():
method = "LogisticRegression"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + "_lyrics" + ".txt"
# setting the model parameters
clf = method_func(penalty='l1',
dual=False,
tol=0.0001,
C=1.0,
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
random_state=None,
solver='liblinear',
max_iter=100,
multi_class='ovr',
verbose=0,
warm_start=False,
n_jobs=1)
# use a full grid search over all parameters
param_grid = {"C": [0.1, 1.0, 10.0]}
run_model_util.run_model_lyrics(clf, param_grid, method, file_name)
def linear_svm_func():
method = "LinearSVC"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + "_lyrics" + ".txt"
# setting the model parameters
clf = method_func(penalty='l2',
loss='squared_hinge',
dual=True,
tol=0.0001,
C=1.0,
multi_class='ovr',
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
verbose=0,
random_state=None,
max_iter=1000)
# use a full grid search over following parameters
param_grid = {"C": [0.5, 1.0]}
run_model_util.run_model_lyrics(clf, param_grid, method, file_name)
def AdaBoost_func():
method = "AdaBoostClassifier"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + "_lyrics" + ".txt"
# setting the model parameters
clf = method_func(base_estimator=None,
n_estimators=50,
learning_rate=1.0,
algorithm='SAMME.R',
random_state=None)
# use a full grid search over all parameters
param_grid = {"n_estimators": np.arange(60, 140, 40)}
run_model_util.run_model_lyrics(clf, param_grid, method, file_name)
def mlp_func():
method = "MLPClassifier"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + "_lyrics" + ".txt"
# setting the model parameters
clf = method_func(activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='adaptive',
learning_rate_init=0.001,
power_t=0.5,
max_iter=400,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
# use a full grid search over all parameters
param_grid = {
"hidden_layer_sizes": [(2000, 20, 4)],
"activation": ['relu', 'logistic']
}
run_model_util.run_model_lyrics(clf, param_grid, method, file_name)