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test_sklearn.py
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test_sklearn.py
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# coding: utf-8
# pylint: skip-file
import math
import os
import unittest
from litemort import (LiteMORT,Mort_Preprocess)
import lightgbm as lgb
import numpy as np
from sklearn.base import clone
from sklearn import preprocessing
from sklearn.datasets import (load_boston, load_breast_cancer, load_digits,
load_iris, load_svmlight_file)
from sklearn.externals import joblib
from sklearn.metrics import log_loss, mean_squared_error
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.utils.estimator_checks import (_yield_all_checks, SkipTest,
check_parameters_default_constructible)
isMORT=True
try:
from sklearn.utils.estimator_checks import check_no_fit_attributes_set_in_init
sklearn_at_least_019 = True
except ImportError:
sklearn_at_least_019 = False
def multi_error(y_true, y_pred):
return np.mean(y_true != y_pred)
def multi_logloss(y_true, y_pred):
return np.mean([-math.log(y_pred[i][y]) for i, y in enumerate(y_true)])
class TestSklearn(unittest.TestCase):
def test_binary_breast(self):
params = {
"objective": "binary", "metric": "logloss",'early_stop': 5, 'num_boost_round': 50,
"verbosity": 1,
}
X, y = load_breast_cancer(True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
if isMORT:
mort = LiteMORT(params)
mort.fit(X_train, y_train, eval_set=[(X_test, y_test)], params=params)
result = mort.predict(X_test)
ret = log_loss(y_test, mort.predict_proba(X_test))
else:
gbm = lgb.LGBMClassifier(n_estimators=50, silent=True)
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
result = gbm.predict(X_test)
ret = log_loss(y_test, gbm.predict_proba(X_test))
self.assertAlmostEqual(ret, gbm.evals_result_['valid_0']['binary_logloss'][gbm.best_iteration_ - 1],places=5)
self.assertLess(ret, 0.15)
def ttest_binary_digits(self):
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
rng = np.random.RandomState(1994)
params = {
"objective": "binary", "metric": "logloss", 'early_stop': 5, 'num_boost_round': 50,
"verbosity": 1,
}
digits = load_digits(2)
y = digits['target']
X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X, y):
#xgb_model = cls(random_state=42).fit(X[train_index], y[train_index])
#xgb_model.predict(X[test_index])
mort = LiteMORT(params).fit(X[train_index], y[train_index])
preds = mort.predict(X[test_index])
labels = y[test_index]
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1
def ttest_regression(self):
params = {
"objective": "regression", 'early_stop': 5, 'num_boost_round': 50, "verbosity": 1,
}
X, y = load_boston(True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
if isMORT:
mort = LiteMORT(params)
mort.fit(X_train, y_train, eval_set=[(X_test, y_test)], params=params)
ret = mean_squared_error(y_test, mort.predict(X_test))
else:
gbm = lgb.LGBMRegressor(n_estimators=50, silent=True)
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
ret = mean_squared_error(y_test, gbm.predict(X_test))
self.assertAlmostEqual(ret, gbm.evals_result_['valid_0']['l2'][gbm.best_iteration_ - 1], places=5)
self.assertLess(ret, 16)
def ttest_regression_boston_housing(self):
rng = np.random.RandomState(1994)
params = {
"objective": "regression", 'early_stop': 5, 'num_boost_round': 50, "verbosity": 1,
}
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
from sklearn.model_selection import KFold
params = {
"objective": "regression", 'early_stop': 5, 'num_boost_round': 50, "verbosity": 1,
}
boston = load_boston()
y = boston['target']
X = boston['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X, y):
#xgb_model = xgb.XGBRegressor().fit(X[train_index], y[train_index])
mort = LiteMORT(params)
mort.fit(X[train_index], y[train_index], params=params)
preds = mort.predict(X[test_index])
labels = y[test_index]
assert mean_squared_error(preds, labels) < 25
#@unittest.skipIf(not litemort.combat.PANDAS_INSTALLED, 'pandas is not installed')
def test_pandas_categorical(self):
params = { #需要更详细的的测试
"objective": "binary", "metric": "logloss", 'early_stop': 5, 'num_boost_round': 50,
"verbosity": 1,
}
import pandas as pd
X = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'c', 'd'] * 75), # str
"B": np.random.permutation([1, 2, 3] * 100), # int
"C": np.random.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60), # float
"D": np.random.permutation([True, False] * 150)}) # bool
y = np.random.permutation([0, 1] * 150)
X_test = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'e'] * 20),
"B": np.random.permutation([1, 3] * 30),
"C": np.random.permutation([0.1, -0.1, 0.2, 0.2] * 15),
"D": np.random.permutation([True, False] * 30)})
if True:
X, X_test = Mort_Preprocess.OrdinalEncode_(X,X_test)
for col in ["A", "B", "C", "D"]:
X[col] = X[col].astype('category')
X_test[col] = X_test[col].astype('category')
#trn_data = lgb.Dataset(X, label=y)
if isMORT:
mort0 = LiteMORT(params).fit(X, y)
pred0 = list(mort0.predict(X_test))
mort1 = LiteMORT(params).fit(X, y, categorical_feature=[0])
pred1 = list(mort1.predict(X_test))
mort2 = LiteMORT(params).fit(X, y, categorical_feature=['A'])
pred2 = list(mort2.predict(X_test))
mort3 = LiteMORT(params).fit(X, y, categorical_feature=['A', 'B', 'C', 'D'])
pred3 = list(mort3.predict(X_test))
else:
clf=lgb.sklearn.LGBMClassifier()
gbm_ = clf.fit(X, y)
gbm0 = lgb.sklearn.LGBMClassifier().fit(X, y)
pred0 = list(gbm0.predict(X_test))
gbm1 = lgb.sklearn.LGBMClassifier().fit(X, y, categorical_feature=[0])
pred1 = list(gbm1.predict(X_test))
gbm2 = lgb.sklearn.LGBMClassifier().fit(X, y, categorical_feature=['A'])
pred2 = list(gbm2.predict(X_test))
gbm3 = lgb.sklearn.LGBMClassifier().fit(X, y, categorical_feature=['A', 'B', 'C', 'D'])
pred3 = list(gbm3.predict(X_test))
gbm3.booster_.save_model('categorical.model')
gbm4 = lgb.Booster(model_file='categorical.model')
pred4 = list(gbm4.predict(X_test))
pred_prob = list(gbm0.predict_proba(X_test)[:, 1])
np.testing.assert_almost_equal(pred_prob, pred4)
input("...")
#np.testing.assert_almost_equal(pred0, pred1)
#np.testing.assert_almost_equal(pred0, pred2)
#np.testing.assert_almost_equal(pred0, pred3)