I'm trying to train a XGBoost model using the params below:
xgb_params = {
'objective': 'binary:logistic',
'eval_metric': 'auc',
'lambda': 0.8,
'alpha': 0.4,
'max_depth': 10,
'max_delta_step': 1,
'verbose': True
}
Since my input data is too big to be fully loaded into the memory, I adapt the incremental training:
xgb_clf = xgb.train(xgb_params, input_data, num_boost_round=rounds_per_batch,
xgb_model=model_path)
The code for prediction is
xgb_clf = xgb.XGBClassifier()
booster = xgb.Booster()
booster.load_model(model_path)
xgb_clf._Booster = booster
raw_probas = xgb_clf.predict_proba(x)
The result seemed good. But when I tried to invoke xgb_clf.get_xgb_params(), I got a param dict in which all params were set to default values.
I can guess that the root cause is when I initialized the model, I didn't pass any params in. So the model was initialized using the default values but when it predicted, it used an internal booster that had been fitted using some pre-defined params.
However, I wonder is there any way that, after I assign a pre-trained booster model to a XGBClassifier, I can see the real params that are used to train the booster, but not those which are used to initialize the classifier.
You seem to be mixing the sklearn API with the functional API in your code, if you stick to either one you should get the parameters to persist in the pickle. Here's an example using the sklearn API.
import pickle
import numpy as np
import xgboost as xgb
from sklearn.datasets import load_digits
digits = load_digits(2)
y = digits['target']
X = digits['data']
xgb_params = {
'objective': 'binary:logistic',
'reg_lambda': 0.8,
'reg_alpha': 0.4,
'max_depth': 10,
'max_delta_step': 1,
}
clf = xgb.XGBClassifier(**xgb_params)
clf.fit(X, y, eval_metric='auc', verbose=True)
pickle.dump(clf, open("xgb_temp.pkl", "wb"))
clf2 = pickle.load(open("xgb_temp.pkl", "rb"))
assert np.allclose(clf.predict(X), clf2.predict(X))
print(clf2.get_xgb_params())
which produces
{'base_score': 0.5,
'colsample_bylevel': 1,
'colsample_bytree': 1,
'gamma': 0,
'learning_rate': 0.1,
'max_delta_step': 1,
'max_depth': 10,
'min_child_weight': 1,
'missing': nan,
'n_estimators': 100,
'objective': 'binary:logistic',
'reg_alpha': 0.4,
'reg_lambda': 0.8,
'scale_pos_weight': 1,
'seed': 0,
'silent': 1,
'subsample': 1}
If you are training like this -
dtrain = xgb.DMatrix(x_train, label=y_train)
model = xgb.train(model_params, dtrain, model_num_rounds)
Then the model returned is a Booster.
import json
json.loads(model.save_config())
the model.save_config() function lists down model parameters in addition to other configurations.
To add to #ytsaig's answer, if you are using early_stopping_rounds argument in clf.fit() method then certain additional parameters are generated but not returned as part of clf.get_xgb_params() method. These can be accessed directly as follows: clf.best_score, clf.best_iteration and clf.best_ntree_limit.
Ref: https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier.fit
Related
I'm building a logistic regression model to predict a binary target feature. I want to try different values of different parameters using the param_grid argument, to find the best fit with the best values. This is my code:
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state = 42)
logModel = LogisticRegression(C = 1, penalty='l1',solver='liblinear');
Grid_params = {
"penalty" : ['l1','l2','elasticnet','none'],
"C" : [0.001, 0.01, 0.1, 1, 10, 100, 1000], # Basically smaller C specify stronger regularization.
'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],
'max_iter' : [50,100,200,500,1000,2500]
}
clf = GridSearchCV(logModel, param_grid=Grid_params, cv = 10, verbose = True, n_jobs=-1,error_score='raise')
clf_fitted = clf.fit(X_train,Y_train)
And this is where I get the error. I have read already that some solvers dont work with l1, and some don't work with l2. How can I tune the param_grid in this case?
I tried also using only simple logModel = LogisticRegression() but didn't work.
Full error:
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.
Gridsearch accepts the list of dicts for that purpose, given you absolutely need to include solvers into grid, you should be able to do something like this:
Grid_params = [
{'solver' : ['saga'],
'penalty' : ['elasticnet', 'l1', 'l2', 'none'],
'max_iter' : [50,100,200,500,1000,2500],
'C' : [0.001, 0.01, 0.1, 1, 10, 100, 1000]},
{'solver' : ['newton-cg', 'lbfgs'],
'penalty' : ['l2','none'],
'max_iter' : [50,100,200,500,1000,2500],
'C' : [0.001, 0.01, 0.1, 1, 10, 100, 1000]},
# add more parameter sets as needed...
]
I am working on a project with a dataset of aircraft engines and their lifetime. I need to use XGBRegressor to have the highest success rate of my model on my validation data.
I am having trouble understanding the XGBRegressor documentation, I was wondering if you know how I could optimize the search for optimal parameters instead of testing everything by hand.
I attached a part of my code related to XGB.
from xgboost import XGBRegressor
xgb = XGBRegressor(learning_rate = 0.3, max_depth = 7, n_estimators = 230, subsample = 0.7, colsample_bylevel = 0.7, colsample_bytree = 0.7, min_child_weight = 4, reg_alpha = 10, reg_lambda = 10)
xgb.fit(X_train, y_train)
The following answer will help you achieve this; you can add more Hyperparameters or more classifiers to test with different approaches. if you set cv=5 it will do 5-fold cross validation; but if you have a specific validation and only want to get perfect results you can add pass this to cv:
indices = np.arange(len(X_train))
train_idx, test_idx = train_test_split(indices, test_size=0.2)
cv_indices=[(train_idx, test_idx)]
otherwise use: cv=5 to do 5-fold CV while searching for parameters.
from sklearn.model_selection import GridSearchCV
from xgboost import XGBRegressor
dict_classifiers = {
"XGB": XGBRegressor()
}
params = {
"XGB": {'min_child_weight': [1, 5, 10],
'gamma': [0.5, 1, 1.5, 2, 5],
'subsample': [0.6, 0.8, 1.0],
'colsample_bytree': [0.6, 0.8, 1.0],
'max_depth': [3, 4, 5], "n_estimators": [300, 600],
"learning_rate": [0.001, 0.01, 0.1],
}
}
for classifier_name in dict_classifiers.keys() & params:
print("training: ", classifier_name)
gridSearch = GridSearchCV(
estimator=dict_classifiers[classifier_name], param_grid=params[classifier_name], cv=cv_indices)
gridSearch.fit(X_train, # shoud have shape of (n_samples, n_features)
y_train.reshape((-1))) #this should be an array with shape (n_samples,)
print(gridSearch.best_score_, gridSearch.best_params_)
I am running this:
# Hyperparameter tuning - Random Forest #
# Hyperparameters' grid
parameters = {'n_estimators': list(range(100, 250, 25)), 'criterion': ['gini', 'entropy'],
'max_depth': list(range(2, 11, 2)), 'max_features': [0.1, 0.2, 0.3, 0.4, 0.5],
'class_weight': [{0: 1, 1: i} for i in np.arange(1, 4, 0.2).tolist()], 'min_samples_split': list(range(2, 7))}
# Instantiate random forest
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(random_state=0)
# Execute grid search and retrieve the best classifier
from sklearn.model_selection import GridSearchCV
classifiers_grid = GridSearchCV(estimator=classifier, param_grid=parameters, scoring='balanced_accuracy',
cv=5, refit=True, n_jobs=-1)
classifiers_grid.fit(X, y)
and I am receiving this warning:
.../anaconda/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536:
FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
TypeError: '<' not supported between instances of 'str' and 'int'
Why is this and how can I fix it?
I had similar issue of FitFailedWarning with different details, after many runs I found, the parameter value passing has the error, try
parameters = {'n_estimators': [100,125,150,175,200,225,250],
'criterion': ['gini', 'entropy'],
'max_depth': [2,4,6,8,10],
'max_features': [0.1, 0.2, 0.3, 0.4, 0.5],
'class_weight': [0.2,0.4,0.6,0.8,1.0],
'min_samples_split': [2,3,4,5,6,7]}
This will pass for sure, for me it happened in XGBClassifier, somehow the values datatype mixing up
One more is if the value exceeds the range, for example in XGBClassifier 'subsample' paramerters max value is 1.0, if it is set as 1.1, FitFailedWarning will occur
For me this was giving same error but after removing none from max_dept it is fitting properly.
param_grid={'n_estimators':[100,200,300,400,500],
'criterion':['gini', 'entropy'],
'max_depth':['None',5,10,20,30,40,50,60,70],
'min_samples_split':[5,10,20,25,30,40,50],
'max_features':[ 'sqrt', 'log2'],
'max_leaf_nodes':[5,10,20,25,30,40,50],
'min_samples_leaf':[1,100,200,300,400,500]
}
code which is running properly:
param_grid={'n_estimators':[100,200,300,400,500],
'criterion':['gini', 'entropy'],
'max_depth':[5,10,20,30,40,50,60,70],
'min_samples_split':[5,10,20,25,30,40,50],
'max_features':[ 'sqrt', 'log2'],
'max_leaf_nodes':[5,10,20,25,30,40,50],
'min_samples_leaf':[1,100,200,300,400,500]
}
I too got same error and when I passed hyperparameters as in MachineLearningMastery, I got output without warning...
Try this way if anyone get similar issues...
# grid search logistic regression model on the sonar dataset
from pandas import read_csv
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.model_selection import GridSearchCV
# load dataset
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/sonar.csv'
dataframe = read_csv(url, header=None)
# split into input and output elements
data = dataframe.values
X, y = data[:, :-1], data[:, -1]
# define model
model = LogisticRegression()
# define evaluation
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# define search space
space = dict()
space['solver'] = ['newton-cg', 'lbfgs', 'liblinear']
space['penalty'] = ['none', 'l1', 'l2', 'elasticnet']
space['C'] = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 10, 100]
# define search
search = GridSearchCV(model, space, scoring='accuracy', n_jobs=-1, cv=cv)
# execute search
result = search.fit(X, y)
# summarize result
print('Best Score: %s' % result.best_score_)
print('Best Hyperparameters: %s' % result.best_params_)
Make sure the y-variable is an int, not bool or str.
Change your last line of code to make the y series a 0 or 1, for example:
classifiers_grid.fit(X, list(map(int, y)))
So I am using scikit-learn pipeline to cut down the steps of writing repetitive code. However I can't see to figure out the way to write a code that unpack the parameters for each regressor.
Before using Pipeline, I write a class to unpack the parameters. That works just fine though I do believe there is a better way to go with this.
I don't want to keep writing manually the parameters
from sklearn.pipeline import make_pipeline
pipe_et = make_pipeline(StandardScaler(), ExtraTreesRegressor(n_estimators = 1000, random_state = Seed))
pipe_rf = make_pipeline(StandardScaler(), XGBRegressor())
This is an example of the parameters I want to be unpacked
rf_params = {'n_estimators': 1000, 'n_jobs': -1, 'warm_start': True, 'max_features':2}
There is no error. I don't want to do extra labor work but I expect **params to work but I don't know how to proceed with that. Please help me with my coding style
You could just loop through your pipe_rf object and get the parameters like this:
for i in pipe_rf:
print (i.get_params())
OUTPUT
{'copy': True, 'with_mean': True, 'with_std': True}
{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bynode': 1, 'colsample_bytree': 1, 'gamma': 0, 'importance_type': 'gain', 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 3, 'min_child_weight': 1, 'missing': None, 'n_estimators': 100, 'n_jobs': 1, 'nthread': None, 'objective': 'reg:linear', 'random_state': 0, 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': None, 'silent': None, 'subsample': 1, 'verbosity': 1}
Hope this helps!
You need to format the parameters of the estimator using __ so that it can be fed as params for pipeline. I have written a small function that can take pipeline and parameters for the estimator, then it would return the appropriate params for the estimator.
Try this example:
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
pipe_rf = make_pipeline(StandardScaler(), RandomForestRegressor())
rf_params = {'n_estimators': 10, 'n_jobs': -1, 'warm_start': True, 'max_features':2}
def params_formatter(pipeline, est_params):
est_name = pipeline.steps[-1][0]
return {'{}__{}'.format(est_name,k):v for k,v in est_params.items()}
params = params_formatter(pipe_rf, rf_params)
pipe_rf.set_params(**params)
# Pipeline(memory=None,
# steps=[('standardscaler',
# StandardScaler(copy=True, with_mean=True, with_std=True)),
# ('randomforestregressor',
# RandomForestRegressor(bootstrap=True, criterion='mse',
# max_depth=None, max_features=2,
# max_leaf_nodes=None,
# min_impurity_decrease=0.0,
# min_impurity_split=None,
# min_samples_leaf=1, min_samples_split=2,
# min_weight_fraction_leaf=0.0,
# n_estimators=10, n_jobs=-1,
# oob_score=False, random_state=None,
# verbose=0, warm_start=True))],
# verbose=False)
I need some help to do a bagging aggregation of different XGBoost models (with types Booster). The idea is after to store one model, the final one, into a pickle file.
I start by creating a dummy dataframe:
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.model_selection import KFold
import pickle
dummy_df = pd.DataFrame(np.random.randn(100, 3), columns=list('ABC'))
dummy_df['D'] = -15 * dummy_df['A'] + 32 * dummy_df['B']
X = dummy_df.drop('D', axis=1)
y = dummy_df['D']
I establish some parameters I'd like to test (resulting for instance from a gridsearch):
params = {'eta': 0.06, # learning rate
'tree_method': "auto",#considering my dummy df, might be more interesting to use "gblinear" of course...
'max_depth': 3,
'subsample': 0.75,
'colsample_bytree': 0.75,
'colsample_bylevel': 0.75,
'min_child_weight': 5,
'alpha': 10,
'objective': 'reg:linear',
'eval_metric': 'rmse',
'random_state': 99,
'silent': True}
Finally, I create my cross-validation scheme:
accu = 0
n_splits = 5
folds = KFold(n_splits=n_splits, shuffle=True, random_state=1)
for n_fold, (train_idx, valid_idx) in enumerate(folds.split(X, y)):
train_x, train_y = X.iloc[train_idx], y.iloc[train_idx]
valid_x, valid_y = X.iloc[valid_idx], y.iloc[valid_idx]
dtrain = xgb.DMatrix(train_x, train_y)
dvalid = xgb.DMatrix(valid_x, valid_y)
watchlist = [(dtrain, 'train'), (dvalid, 'valid')]
model = xgb.train(params, dtrain, 2500, watchlist, maximize=False, early_stopping_rounds=40, verbose_eval=50)
if accu == 0:
model_to_save = model
accu += 1
else:
model_to_save += model
It trains properly for the first and second iterations in my for loop, but when needs to add the 2 first iterations (final line), I get the following error:
TypeError: unsupported operand type(s) for +=: 'Booster' and 'Booster'
Is there any way in Python to add 2 Boosters? And also to divide a Booster by an integer since I'll have to divide at the end model_to_save by n_splits?
PS: Storing all the XGBoost models is not an option considering other constraints I can face later on.
params = {'eta': 0.06, # learning rate
'tree_method': "auto",#considering my dummy df, might be more interesting to use "gblinear" of course...
'max_depth': 3,
'subsample': 0.75,
'colsample_bytree': 0.75,
'colsample_bylevel': 0.75,
'min_child_weight': 5,
'alpha': 10,
'objective': 'reg:linear',
'eval_metric': 'rmse',
'random_state': 99,
'silent': True}