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 + ".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, 5.0]}
run_model_util.run_model(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 + ".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(20,180,40),
"learning_rate": [ 0.1, 1]}
run_model_util.run_model(clf, param_grid, method, file_name)
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 + ".txt"
# setting the model parameters
clf = method_func(
max_depth=3, 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": [3, 10],
"n_estimators": [100, 200]}
run_model_util.run_model(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 + ".txt"
# setting the model parameters
clf = method_func(
penalty='l2', 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],
"penalty": ['l2', 'l1']}
run_model_util.run_model(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 + ".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": [3, 10],
"n_estimators": [50, 200]}
run_model_util.run_model(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 + ".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 = {"n_estimators": (10,100),
"max_depth": (3,10),
"criterion": ["gini", "entropy"]}
run_model_util.run_model(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 + ".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": [(15,6),(40,6)],
"activation": ['relu', 'logistic']
}
run_model_util.run_model(clf, param_grid, method, file_name)
import sys, os
import time
sys.path.append(os.path.realpath("%s/.." % os.path.dirname(__file__)))
from util import data_accessor_util
from util import run_model_util
from sklearn.ensemble import GradientBoostingClassifier
# ----------------
# model
# ----------------
method = "GradientBoostingClassifier"
# create file_name for saving results
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + ".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": [3, 10], "n_estimators": [50, 200]}
run_model_util.run_model(clf, param_grid, method)
@author - Farzan Memarian
"""
import sys, os
import time
sys.path.append(os.path.realpath("%s/.." % os.path.dirname(__file__)))
from util import data_accessor_util
from util import run_model_util
from sklearn.svm import LinearSVC
# ----------------
# models
# ----------------
method = "LinearSVC"
possibles = globals().copy()
possibles.update(locals())
method_func = possibles.get(method)
file_name = method + ".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 = {"penalty": []}
run_model_util.run_model(clf, param_grid, method, file_name)