def test_silent():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
tmpfile = 'test_data_dumps'
with LogStdout(open(tmpfile, 'w')):
model = CatBoost(dict(iterations=10, silent=True))
model.fit(pool)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 0)
with LogStdout(open(tmpfile, 'w')):
model = CatBoost(dict(iterations=10, silent=True))
model.fit(pool, silent=False)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 10)
with LogStdout(open(tmpfile, 'w')):
train(pool, {'silent': True})
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 0)
with LogStdout(open(tmpfile, 'w')):
model = CatBoost(dict(iterations=10, silent=False))
model.fit(pool, silent=True)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 0)
with LogStdout(open(tmpfile, 'w')):
model = CatBoost(dict(iterations=10, verbose=5))
model.fit(pool, silent=True)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 0)
def cat_cv(train, test, params, fit_params, cat_features, feature_names, nfold,
seed):
train.Pred = pd.DataFrame({
'id': train['样本id'],
'true': train['收率'],
'pred': np.zeros(len(train))
})
test.Pred = pd.DataFrame({'id': test['样本id'], 'pred': np.zeros(len(test))})
kfolder = KFold(n_splits=nfold, shuffle=True, random_state=seed)
cat_tst = cat.Pool(data=test[feature_names],
cat_features=cat_features,
feature_names=feature_names)
for fold_id, (trn_idx, val_idx) in enumerate(kfolder.split(train['收率'])):
print(f'\nFold_{fold_id} Training ================================\n')
cat_trn = cat.Pool(data=train.iloc[trn_idx][feature_names],
label=train.iloc[trn_idx]['收率'],
cat_features=cat_features,
feature_names=feature_names)
cat_val = cat.Pool(data=train.iloc[val_idx][feature_names],
label=train.iloc[val_idx]['收率'],
cat_features=cat_features,
feature_names=feature_names)
cat.train(params=params, pool=cat_trn, **fit_params, eval_set=cat_val)
val_pred = cat.predict(train.iloc[val_idx][feature_names])
train.Pred.loc[val_idx, 'pred'] = val_pred
print(f'Fold_{fold_id}', mse(train.iloc[val_idx]['收率'], val_pred))
test.Pred['pred'] += cat.predict(cat_tst) / nfold
print('\n\nCV LOSS:', mse(train.Pred['true'], train.Pred['pred']))
return test.Pred
def test_verbose_int(verbose):
expected_line_count = {5: 2, False: 0, True: 10}
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
tmpfile = 'test_data_dumps'
with LogStdout(open(tmpfile, 'w')):
cv(pool, {
"iterations": 10,
"random_seed": 0,
"loss_function": "Logloss"
},
verbose=verbose)
with open(tmpfile, 'r') as output:
assert (sum(1 for line in output) == expected_line_count[verbose])
with LogStdout(open(tmpfile, 'w')):
train(pool, {
"iterations": 10,
"random_seed": 0,
"loss_function": "Logloss"
},
verbose=verbose)
with open(tmpfile, 'r') as output:
assert (sum(1 for line in output) == expected_line_count[verbose])
return local_canonical_file(remove_time_from_json(JSON_LOG_PATH))
def test_catboost_numerical_validation():
ds = vaex.ml.datasets.load_iris()
features = ['sepal_width', 'petal_length', 'sepal_length', 'petal_width']
# Vanilla catboost
dtrain = cb.Pool(ds[features].values, label=ds.data.class_)
cb_bst = cb.train(params=params_multiclass,
dtrain=dtrain,
num_boost_round=3)
cb_pred = cb_bst.predict(dtrain, prediction_type='Probability')
# catboost through vaex
booster = vaex.ml.catboost.CatBoostModel(features=features,
params=params_multiclass,
num_boost_round=3)
booster.fit(ds, ds.class_)
vaex_pred = booster.predict(ds)
# Comparing the the predictions of catboost vs vaex.ml
np.testing.assert_equal(
vaex_pred,
cb_pred,
verbose=True,
err_msg=
'The predictions of vaex.ml.catboost do not match those of pure catboost'
)
def cgb_cv(df, features, categorical_features, n_folds, param):
kf = GroupKFold(n_splits=n_folds)
group_map = dict(zip(np.arange(1, 13),
pd.cut(np.arange(1, 13), n_folds, labels=np.arange(n_folds))))
group = df.timestamp.dt.month.map(group_map)
models = []
train_scores = []
valid_scores = []
for train_index, val_index in kf.split(df, df['building_id'], groups=group):
train_X, train_y = df[features].iloc[train_index], df['meter_reading'].iloc[train_index]
val_X, val_y = df[features].iloc[val_index], df['meter_reading'].iloc[val_index]
cgb_train = cgb.Pool(train_X, train_y, cat_features=categorical_features)
cgb_eval = cgb.Pool(val_X, val_y, cat_features=categorical_features)
gbm = cgb.train(cgb_train, param, eval_set=cgb_eval, verbose=20)
train_preds = gbm.predict(train_X)
if use_log1p_target:
train_preds = np.expm1(train_preds)
train_y = np.expm1(train_y)
train_scores.append(rmsle(train_y, train_preds))
valid_preds = gbm.predict(val_X)
if use_log1p_target:
valid_preds = np.expm1(valid_preds)
val_y = np.expm1(val_y)
valid_scores.append(rmsle(val_y, valid_preds))
models.append(gbm)
return train_scores, valid_scores, models
def fit(self, data, clf=None):
setseed(self.seed)
train_df = data[data['visitId'].\
apply(lambda x: x.date()) >= datetime.date(2016,9,30)]
val_df = data[data['visitId'].\
apply(lambda x: x.date()) < datetime.date(2016,9,30)]
if clf:
train_X, train_y = \
train_df[self.features_name].values,\
train_df['validRevenue'].values\
val_X, val_y = \
val_df[self.features_name].values,\
val_df['validRevenue'].values
else:
train_X, train_y = \
train_df[self.features_name].values,\
train_df['totals_transactionRevenue'].values
val_X, val_y = \
val_df[self.features_name].values,\
val_df['totals_transactionRevenue'].values
# x_train, x_eval, y_train, y_eval = \
# train_test_split(df_x, df_y,
# test_size = self.test_ratio,
# random_state = self.seed)
cat_train = cat.Pool(data=train_X,
label=train_y,
feature_names=self.features_name,
cat_features=self.categorical_feature)
cat_eval = cat.Pool(data=val_X,
label=val_y,
feature_names=self.features_name,
cat_features=self.categorical_feature)
model_param = self.params['params_clf'] if clf else self.params[
'params_reg']
self.estimator = cat.train(
params=model_param,
pool=cat_train,
eval_set=cat_eval,
# num_boost_round = self.num_boost_round,
# learning_rate = self.params['learning_rate'],
# max_depth = self.params['max_depth'],
# l2_leaf_reg = self.params['l2_leaf_reg'],
# rsm = self.params['colsample_ratio'],
# subsample = self.params['subsample_ratio'],
# class_weights = self.params['class_weights'],
# loss_function = self.loglikeloss,
# custom_loss = self.loglikeloss,
# custom_metric = self.roc_auc_error,
# eval_metric = self.roc_auc_error
)
return self
def cat_cv_train(X_train, y_train, params, kfold):
'''
taking feature and target dataset and using cross-validation method to train model, return list of classifiers, length of list is the number of clfs for each fold.
'''
from catboost import train, Pool
from sklearn.model_selection import train_test_split, KFold
features = [feature for feature in X_train.columns if feature not in ['target', 'card_id', 'first_active_month']]
categorical_features = [feature for feature in features if 'feature_' in feature]
folds = KFold(n_splits=kfold, shuffle=True, random_state=42)
clf_list = []
for train_idxs, val_idxs in folds.split(X_train.values, y_train.values):
# training set
train_set = Pool(data=X_train.iloc[train_idxs][features],\
label=y_train.iloc[train_idxs],\
cat_features=categorical_features)
# validation set
valid_set = Pool(data=X_train.iloc[val_idxs][features],\
label=y_train.iloc[val_idxs],\
cat_features=categorical_features)
# train clf
clf = train(pool=train_set,\
params=params,\
verbose=100,\
iterations=10000,\
eval_set=valid_set)
# add current clf to clf_list
clf_list.append(clf)
return clf_list
def main():
print("load train test datasets")
train, test = load_train_test()
y_train_all = train['orderType']
id_test = test['userid']
del train['orderType']
df_columns = train.columns.values
print('===> feature count: {}'.format(len(df_columns)))
cat_params = {
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'learning_rate': 0.1,
'l2_leaf_reg': 5, # L2 regularization coefficient.
'subsample': 0.9,
'depth': 8, # Depth of the tree
'border_count': 255, # The number of splits for numerical features
'thread_count': 6,
'train_dir': 'catboost_train_logs',
'bootstrap_type': 'Bernoulli',
'use_best_model': True,
'random_seed': 42
}
pool = Pool(train, y_train_all)
cv_results = cat.train(pool=pool,
params=cat_params,
num_boost_round=4000,
nfold=5,
seed=42,
stratified=True)
def _cv(self, dataset: tk.data.Dataset,
folds: tk.validation.FoldsType) -> None:
import catboost
assert isinstance(dataset.data, pd.DataFrame)
self.train_pool_ = catboost.Pool(
data=dataset.data,
label=dataset.labels,
group_id=dataset.groups,
feature_names=dataset.data.columns.values.tolist(),
cat_features=dataset.data.select_dtypes("object").columns.values,
)
self.gbms_, score_list = [], []
for fold, (train_indices, val_indices) in enumerate(folds):
with tk.log.trace(f"fold{fold}"):
gbm = catboost.train(
params=self.params,
pool=self.train_pool_.slice(train_indices),
eval_set=self.train_pool_.slice(val_indices),
**(self.cv_params or {}),
)
self.gbms_.append(gbm)
score_list.append(gbm.get_best_score()["validation"])
cv_weights = [len(val_indices) for _, val_indices in folds]
evals: tk.evaluations.EvalsType = {}
for k in score_list[0]:
score = [s[k] for s in score_list]
score = np.float32(np.average(score, weights=cv_weights))
evals[k] = score
logger.info(f"cv: {tk.evaluations.to_str(evals)}")
def test_verbose_int(verbose):
expected_line_count = {5: 3, False: 0, True: 10}
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
tmpfile = 'test_data_dumps'
with LogStdout(open(tmpfile, 'w')):
cv(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss"}, verbose=verbose)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == expected_line_count[verbose])
with LogStdout(open(tmpfile, 'w')):
train(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss"}, verbose=verbose)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == expected_line_count[verbose])
return local_canonical_file(remove_time_from_json(JSON_LOG_PATH))
def generate_submit(self, num_boost_round=None, from_model_saved=False):
assert num_boost_round is not None
if not from_model_saved:
dtrain = self.get_train_set(as_cgb_pool=True)
booster = cgb.train(
dtrain=dtrain,
params=self.params_best_fit,
num_boost_round=num_boost_round)
self.save_model(booster)
else:
booster = cgb.CatBoost(model_file=from_model_saved)
dftest = self.get_test_set(as_cgb_pool=True)
with Timer("Predicting"):
probas = booster.predict(dftest, prediction_type="Probability")
dfpred = pd.DataFrame(probas)[[1]] # Get proba classe one
dfpred = dfpred.rename(columns={1: 'target'})
now = pd.Timestamp.now(tz='CET').strftime("%d-%Hh-%Mm")
fpath = RESULT_DIR / "catboost_submit_{}.csv".format(now)
with Timer('Storing in {}'.format(fpath)):
dfpred.to_csv(fpath, index=False)
def train_model(
df_train,
df_valid,
model_params,
general_params,
):
train_pool = catboost.Pool(df_train["text"].values,
label=df_train["label"].values,
text_features=[0])
valid_pool = catboost.Pool(df_valid["text"].values,
label=df_valid["label"].values,
text_features=[0])
model_params = copy.deepcopy(model_params)
model_params.update({"train_dir": general_params["logdir"]})
model = catboost.train(
pool=train_pool,
eval_set=valid_pool,
params=model_params,
verbose=False,
plot=False,
)
model.save_model(os.path.join(general_params["logdir"], "model.cbm"))
def test_shap_complex_ctr():
pool = Pool([[0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 1, 2]], [0, 0, 5, 8], cat_features=[0, 1, 2])
model = train(pool, {'random_seed': 12302113, 'iterations': 100})
shap_values = model.get_feature_importance(fstr_type=EFstrType.ShapValues, data=pool)
predictions = model.predict(pool)
assert(len(predictions) == len(shap_values))
for pred_idx in range(len(predictions)):
assert(abs(sum(shap_values[pred_idx]) - predictions[pred_idx]) < 1e-9)
np.savetxt(FIMP_TXT_PATH, shap_values)
return local_canonical_file(FIMP_TXT_PATH)
def train(self, train_df, eval_df, params=None, use_best_eval=True):
self.best_round = None
dtrain = cat.Pool(data=train_df.drop(columns=[self.label]),
label=train_df[self.label])
deval = cat.Pool(data=eval_df.drop(columns=[self.label]),
label=eval_df[self.label])
if params is None:
use_params = deepcopy(self.opt_params)
else:
use_params = deepcopy(params)
num_round = use_params.pop('num_round')
if use_best_eval:
with io.StringIO() as buf, redirect_stdout(buf):
self.clf = cat.train(params=use_params,
pool=dtrain,
evals=deval,
num_boost_round=num_round)
output = buf.getvalue().split("\n")
min_error = np.inf
min_index = 0
for idx in range(1, num_round + 1):
if len(output[idx].split("\t")) == 6:
temp = 1 - float(output[idx].split("\t")[2].split(":")[1])
if min_error > temp:
min_error = temp
min_index = int(output[idx].split("\t")[0][:-1])
print("The minimum is attained in round %d" % (min_index + 1))
self.best_round = min_index + 1
return output
else:
with io.StringIO() as buf, redirect_stdout(buf):
self.clf = cat.train(params=use_params,
pool=dtrain,
evals=deval,
num_boost_round=num_round)
output = buf.getvalue().split("\n")
self.best_round = num_round
return output
def train(self, X_train, X_valid, y_train, y_valid):
""" ccc """
# Convert data to CatBoost Pool format.
ds_train = ctb.Pool(X_train, y_train)
ds_valid = ctb.Pool(X_valid, y_valid)
# Set context dependent CatBoost parameters.
self.params['dtrain'] = ds_train
self.params['eval_set'] = ds_valid
# Train using parameters sent by the user.
return ctb.train(**self.params)
def cv_helper(one_hot_max_size,\
depth,\
l2_leaf_reg,\
random_strength,\
bagging_temperature):
# entire date for evaluate clf training performance
all_data = Pool(data=X_train[features],\
label=y_train,\
cat_features=categorical_features)
# validation RMSE
RMSE = []
for train_idxs, val_idxs in folds.split(X_train.values, y_train.values):
# training set
train_data = Pool(data=X_train.iloc[train_idxs][features],\
label=y_train.iloc[train_idxs],\
cat_features=categorical_features)
# validation set
val_data = Pool(data=X_train.iloc[val_idxs][features],\
label=y_train.iloc[val_idxs],\
cat_features=categorical_features)
# hyperparameters
params = {
'eval_metric': 'RMSE',
'use_best_model': True,
'loss_function': 'RMSE',
'learning_rate': 0.02,
'early_stopping_rounds': 400,
'border_count': 254,
'task_type': 'GPU',
'one_hot_max_size': int(one_hot_max_size),
'depth': int(depth),
'l2_leaf_reg': l2_leaf_reg,
'random_strength': random_strength,
'bagging_temperature': bagging_temperature
}
# classifier
clf = train(pool=train_data,\
params=params,\
verbose=200,\
iterations=10000,\
eval_set=all_data)
# add current fold RMSE on all_data
RMSE.append(clf.best_score_['validation_0']['RMSE'])
return -np.mean(np.array(RMSE))
def validate(self, save_model=True, **kwargs):
dtrain, dtest = self.get_train_valid_set(as_cgb_pool=True)
watchlist = [dtrain, dtest]
booster = cgb.train(
dtrain=dtrain,
params=self.params_best_fit,
eval_set=watchlist,
**kwargs,
)
if save_model:
self.save_model(booster)
return booster
def _train_model(self, data):
print(self.params)
dtrain = cb.Pool(data.X_train, data.y_train)
if data.task == 'Ranking':
dtrain.set_group_id(data.groups)
start = time.time()
self.model = cb.train(
pool=dtrain,
params=self.params,
)
elapsed = time.time() - start
return elapsed
def cgb_fit(config, X_train, y_train):
"""模型（交叉验证）训练，并返回最优迭代次数和最优的结果。
Args:
config: xgb 模型参数 {params, max_round, cv_folds, early_stop_round, seed, save_model_path}
X_train：array like, shape = n_sample * n_feature
y_train: shape = n_sample * 1
Returns:
best_model: 训练好的最优模型
best_auc: float, 在测试集上面的 AUC 值。
best_round: int, 最优迭代次数。
"""
params = config.params
max_round = config.max_round
cv_folds = config.cv_folds
seed = config.seed
save_model_path = config.save_model_path
if cv_folds is not None:
dtrain = cgb.Pool(X_train, label=y_train)
cv_result = cgb.cv(dtrain, params, num_boost_round=max_round, nfold=cv_folds, seed=seed, logging_level='Verbose')
# 最优模型，最优迭代次数
auc_test_avg = cv_result['AUC_test_avg']
best_round = np.argmax(auc_test_avg)
best_auc = np.max(auc_test_avg) # 最好的 auc 值
best_model = cgb.train(dtrain, params, num_boost_round=best_round)
else:
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=100)
dtrain = cgb.Pool(X_train, label=y_train)
dvalid = cgb.Pool(X_valid, label=y_valid)
best_model = cgb.train(params, dtrain, num_boost_round=max_round, eval_set=dvalid)
best_round = best_model.best_iteration
best_auc = best_model.best_score
cv_result = None
if save_model_path:
check_path(save_model_path)
pickle.dump(best_model, open(save_model_path, 'wb'))
return best_model, best_auc, best_round, cv_result
def fit(self,
df,
target,
evals=None,
early_stopping_rounds=None,
verbose_eval=None,
plot=False,
**kwargs):
'''Fit the CatBoostModel model given a DataFrame.
This method accepts all key word arguments for the catboost.train method.
:param df: A vaex DataFrame containing the training features.
:param target: The column name of the target variable.
:param evals: A list of DataFrames to be evaluated during training.
This allows user to watch performance on the validation sets.
:param int early_stopping_rounds: Activates early stopping.
:param bool verbose_eval: Requires at least one item in *evals*.
If *verbose_eval* is True then the evaluation metric on the validation set is printed at each boosting stage.
:param bool plot: if True, display an interactive widget in the Jupyter
notebook of how the train and validation sets score on each boosting iteration.
'''
# Ensure strings
target = vaex.dataframe._ensure_string_from_expression(target)
data = df[self.features].values
target_data = df[target].values
dtrain = catboost.Pool(data=data,
label=target_data,
**self.pool_params)
if evals is not None:
for i, item in enumerate(evals):
data = item[self.features].values
target_data = item[target].values
evals[i] = catboost.Pool(data=data,
label=target_data,
**self.pool_params)
# This does the actual training/fitting of the catboost model
self.booster = catboost.train(
params=self.params,
dtrain=dtrain,
num_boost_round=self.num_boost_round,
evals=evals,
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
plot=plot,
**kwargs)
def test(self, X_test, y_test):
"""This function evaluates the model on paramters and estimators
Parameters
----------
x_test: test set; y_test: test label"""
self.cat = cb.train(params=self.params, pool=self.train_set)
self.predictions = self.cat.predict(X_test,
prediction_type="Probability")
self.predictions = self.predictions[:, 1]
self.y_test = y_test
self.X_test = X_test
print(
"Model will be trained with best parameters obtained from your choice of optimization model ... \n\n\n"
)
print(
"Model trained with {} estimators on the following parameters: \n{}"
.format(self.estimator, self.params))
def cat_stacking(
self,
target=None,
):
X = self.train.drop(self.cols_to_drop, axis=1)
y = self.train[target].values
cat_models = []
predict_train = []
predict_test = []
predict_val = []
for fold, (train_ids, val_ids) in enumerate(self.folds.split(X, y)):
dtrain = cat.Pool(X.iloc[train_ids], y[train_ids])
dval = cat.Pool(X.iloc[val_ids], y[val_ids])
model = cat.train(params=self.cat_params,
dtrain=dtrain,
eval_set=dval,
verbose=200,
early_stopping_rounds=100)
pred = model.predict((cat.Pool(X.iloc[val_ids])))
predict_train = np.concatenate([pred, predict_train])
if self.mode == 'val':
predict_val.append(
model.predict(
cat.Pool(self.val.drop(self.cols_to_drop, axis=1))))
if self.mode == 'test':
predict_test.append(
model.predict(
cat.Pool(self.test.drop(self.drop_columns_test,
axis=1))))
cat_models.append(model)
clear_output()
if self.mode == 'val':
return predict_train, np.asarray(predict_val).mean(axis=0)
if self.mode == 'test':
return predict_train, np.asarray(predict_test).mean(axis=0)
def train_for_threshold(self, features, target='label', num=35000):
train_df = self.train_[self.train_.ID < num]
val_df = self.train_[self.train_.ID >= num]
X_train, y_train = train_df[features].values, train_df[target].values.astype('uint8')
X_eval, y_eval = val_df[features].values, val_df[target].values.astype('uint8')
cat_train = Pool(X_train, y_train)
cat_eval = Pool(X_eval, y_eval)
cat_model = catboost.train(cat_train, self.params, iterations=10000,
eval_set=cat_eval,
early_stopping_rounds=200,
verbose=500)
y_pred = cat_model.predict(cat_eval, prediction_type='Probability')[:,1]
## 获取验证集的真实实体，以及按顺序排序预测的概率和对应的单词
gt_ent, pred_words, pred_proba = sort_val(val_df, y_pred)
## 获取搜索得到的阈值结果
self.threshold, _ = find_threshold(gt_ent, pred_words, pred_proba)
return self.threshold
def train_and_predict(self, features, target='label', save=True):
self.fe = features
X_train, y_train = self.train_[features].values, self.train_[target].values.astype('uint8')
X_test = self.test_[self.fe].values
cat_all = Pool(X_train, y_train)
model = catboost.train(
cat_all,
self.params,
iterations=10000,
early_stopping_rounds=200,
verbose=1000
)
self.model = model
if save:
save_pkl(model, os.path.join(self.opt['model_train'], 'cat.pkl'))
self.pred = model.predict(X_test, prediction_type='Probability')[:, 1]
return self.pred
def test_verbose_int():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
tmpfile = 'test_data_dumps'
with LogStdout(open(tmpfile, 'w')):
cv(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss"}, verbose=5)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 2)
with LogStdout(open(tmpfile, 'w')):
cv(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss"}, verbose=False)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 0)
with LogStdout(open(tmpfile, 'w')):
cv(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss"}, verbose=True)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 10)
log_files = []
for i in range(3):
log_files.append(JSON_LOG_PATH[:-5]+str(i)+JSON_LOG_PATH[-5:])
with LogStdout(open(tmpfile, 'w')):
train(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss", "json_log": log_files[0]}, verbose=5)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 2)
with LogStdout(open(tmpfile, 'w')):
train(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss", "json_log": log_files[1]}, verbose=False)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 0)
with LogStdout(open(tmpfile, 'w')):
train(pool, {"iterations": 10, "random_seed": 0, "loss_function": "Logloss", "json_log": log_files[2]}, verbose=True)
with open(tmpfile, 'r') as output:
assert(sum(1 for line in output) == 10)
canonical_files = []
for log_file in log_files:
canonical_files.append(local_canonical_file(remove_time_from_json(log_file)))
return canonical_files
print('All Iterations')
display(gs_summary)
print('Best parameters: ')
best_cv = gs_results.loc[gs_results['result'].idxmax()]
display(best_cv)
profile.End()
print('Time elapsed: %s mins' % str(profile.ElapsedMinutes))
# Save CV process
gs_summary.to_csv('../AllData_v2_CATBOOST_GS.csv')
# Generate model by best iteration
model = catb.train(params=params,
pool=cat_train,
num_boost_round=best_cv[1],
logging_level="Verbose")
# Save model for possible coded ensemble
model.save_model('../AllData_v2_CATBOOST_Model')
# Generate train prediction for future ensemble
train_preds = model.predict(train_X, prediction_type="Probability")
data = pd.read_csv('../input/application_train.csv')
data['preds'] = train_preds[:, 1]
data = data[['SK_ID_CURR', 'preds']]
data.to_csv('../AllData_v2_CATBOOST_TrainPreds.csv', index=False)
# Generate sub prediction for Kaggle
sub_preds = model.predict(test_X, prediction_type="Probability")
def blending_test(train_x,train_y,val_x,val_y,test_x,weights=(0.38,0.18,0.14,0.1,0.20)):
import random
import lightgbm as lgb
import xgboost as xgb
import catboost as cat
from sklearn.ensemble import RandomForestClassifier as rf
from sklearn.linear_model import LogisticRegression as lr
import gc
res = 0
lgb_params = {
# "max_bin": 512,
"learning_rate": 0.01,
"boosting_type": "gbdt",
"objective": "binary",
"metric": "auc",
"num_leaves": 31,
"max_depth": -1,
"verbose": 200,
"subsample": 0.8,
"colsample_bytree": 0.9,
"subsample_freq": 1,
"reg_alpha": 0,
"min_child_weight": 25,
"random_state": random.randint(1,1000),
"reg_lambda": 1,
"n_jobs": -1,
}
d_train = lgb.Dataset(train_x, label=train_y)
d_test = lgb.Dataset(val_x, label=val_y)
clf_lgb = lgb.train(lgb_params, d_train, 3000, valid_sets=[d_train, d_test], early_stopping_rounds=100,
verbose_eval=200)
# temp_score_val = clf_lgb.best_score["valid_1"]["auc"]
# temp_score_train = clf_lgb.best_score["training"]["auc"]
# weight = 2*(temp_score_train*temp_score_val)/(temp_score_train+temp_score_val)
temp_predict_lgb = clf_lgb.predict(test_x, num_iteration=clf_lgb.best_iteration)
res+=temp_predict_lgb*weights[0]
# weight = (temp_score_train + 2 * temp_score_val)/3
# res_temp_lgb = temp_predict_lgb * temp_predict_lgb * weight
del d_test,d_train,clf_lgb,lgb_params
cat_params = {
"verbose": 200,
"loss_function": "Logloss",
"eval_metric": "AUC",
"iterations": 1000,
"random_seed": random.randint(1,1000),
"learning_rate": 0.03,
"depth": 6,
"thread_count": 16,
"use_best_model": True,
"od_type": 'Iter',
"od_wait": 30,
}
clf_cat = cat.train(pool=(train_x, train_y),params=cat_params,eval_set=[(train_x, train_y),(val_x, val_y)],verbose_eval=200)
# temp_score_val = clf_cat.get_test_eval()[1]
# temp_score_train = clf_cat.get_test_eval()[0]
temp_predict_cat = clf_cat.predict(test_x,prediction_type="Probability")[:,1]
res+=temp_predict_cat*weights[1]
# weight = (temp_score_train + 2 * temp_score_val)/3
# res_temp_cat = temp_predict_cat * temp_predict_cat * weight
del clf_cat,cat_params
gc.collect()
# xgb_params = {
# "objective": "binary:logistic",
# "eval_metric": "auc",
# "n_estimators": 3000,
# "booster":"gbtree",
# "learning_rate": 0.05,
# "max_depth": 6,
# "n_jobs": -1,
# "colsample_bytree": 0.9,
# "subsample": 0.8,
# "min_child_weight": 1,
# "reg_lambda": 1,
# }
# clf_xgb = xgb.XGBClassifier(**xgb_params)
# clf_xgb.fit(X=train_x, y=train_y, eval_set=[(train_x,train_y),(val_x,val_y)],eval_metric="auc",verbose=False,early_stopping_rounds=100)
# temp_score_val = clf_xgb.best_score["valid_1"]["auc"]
# temp_score_train = clf_xgb.best_score["training"]["auc"]
# # weight = 2*(temp_score_train*temp_score_val)/(temp_score_train+temp_score_val)
# temp_predict_xgb = clf_xgb.predict(test_x,ntree_limit=clf_xgb.best_ntree_limit)
# weight = (temp_score_train + 2 * temp_score_val)/3
# res_temp_xgb = temp_predict_xgb * temp_predict_xgb * weight
# del clf_xgb,temp_score_val,temp_score_train,temp_predict_xgb,xgb_params
rf_ = rf(
n_estimators=3000,
criterion='gini',
max_depth=6,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=0.6,
min_impurity_decrease=0.00001,
n_jobs=-1,
verbose=200,
random_state=random.randint(1,1000),
warm_start=True,
)
clf_rf = stop_early_wrapper(rf_,train_x,train_y,val_x, val_y,n_min_iterations=100,scale=1.0001,eval_metric="auc")
temp_predict_rf = clf_rf.predict_proba(test_x)[:,1]
res+=temp_predict_rf*weights[2]
# weight = clf_rf.best_score
# res_temp_rf = temp_predict_rf * temp_predict_rf * weight
del clf_rf,rf_,
gc.collect()
for f in test_x.columns:
train_x[f] = (train_x[f] - train_x[f].min()) / (train_x[f].max() - train_x[f].min())
val_x[f] = (val_x[f] - val_x[f].min()) / (val_x[f].max() - val_x[f].min())
test_x[f] = (test_x[f] - test_x[f].min()) / (test_x[f].max() - test_x[f].min())
clf_lr = lr(penalty='l2', dual=False, tol=0.00001, C=0.06, random_state=random.randint(1,1000),
solver='liblinear', max_iter=2000,
verbose=200)
temp_train = pd.concat([train_x,val_x],axis=0)
temp_y = pd.concat([train_y,val_y],axis=0)
clf_lr.fit(temp_train,temp_y)
print("validation score of lr",clf_lr.score(val_x,val_y))
# val_y_hat = clf_lr.predict_proba(test_x)[:, 1]
# from sklearn.metrics import roc_auc_score
# weight = roc_auc_score(val_y, val_y_hat)
temp_predict_lr = clf_rf.predict_proba(test_x)[:, 1]
res+=temp_predict_lr*weights[3]
# res_temp_lr = temp_predict_lr * temp_predict_lr * weight
del clf_lr,temp_train,temp_y
gc.collect()
from keras.callbacks import ModelCheckpoint
from keras.callbacks import EarlyStopping
clf_nn = KerasClassifier_wrapper(train_x.shape[1])
model_path = "../input/keras_model.h5"
callbacks = [
EarlyStopping(
monitor='val_auc',
patience=20,
mode='max',
verbose=100),
ModelCheckpoint(
model_path,
monitor='val_auc',
save_best_only=True,
mode='max',
verbose=100)
]
# fit estimator
history = clf_nn.fit(
train_x,
train_y,
epochs=1000,
batch_size=1024,
validation_data=(val_x, val_y),
verbose=1,
callbacks=callbacks,
shuffle=True
)
print(history.history.keys())
import matplotlib.pyplot as plt
# summarize history for R^2
fig_acc = plt.figure(figsize=(10, 10))
plt.plot(history.history['auc'])
plt.plot(history.history['val_auc'])
plt.title('model auc')
plt.ylabel('auc')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
fig_acc.savefig("model_auc.png")
# summarize history for loss
fig_loss = plt.figure(figsize=(10, 10))
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
fig_loss.savefig("model_loss.png")
temp_predict_nn = clf_nn.predict_proba(test_x)[:,1]
res+=temp_predict_nn*weights[4]
del history,clf_nn,train_x,train_y,val_x,val_y,test_x
gc.collect()
return res
def fit(self,
df,
evals=None,
early_stopping_rounds=None,
verbose_eval=None,
plot=False,
progress=None,
**kwargs):
'''Fit the CatBoostModel model given a DataFrame.
This method accepts all key word arguments for the catboost.train method.
:param df: A vaex DataFrame containing the features and target on which to train the model.
:param evals: A list of DataFrames to be evaluated during training.
This allows user to watch performance on the validation sets.
:param int early_stopping_rounds: Activates early stopping.
:param bool verbose_eval: Requires at least one item in *evals*.
If *verbose_eval* is True then the evaluation metric on the validation set is printed at each boosting stage.
:param bool plot: if True, display an interactive widget in the Jupyter
notebook of how the train and validation sets score on each boosting iteration.
:param progress: If True display a progressbar when the training is done in batches.
'''
self.pool_params['feature_names'] = self.features
if evals is not None:
for i, item in enumerate(evals):
data = item[self.features].values
target_data = item[self.target].to_numpy()
evals[i] = catboost.Pool(data=data,
label=target_data,
**self.pool_params)
# This does the actual training/fitting of the catboost model
if self.batch_size is None:
data = df[self.features].values
target_data = df[self.target].to_numpy()
dtrain = catboost.Pool(data=data,
label=target_data,
**self.pool_params)
model = catboost.train(params=self.params,
dtrain=dtrain,
num_boost_round=self.num_boost_round,
evals=evals,
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
plot=plot,
**kwargs)
self.booster = model
self.evals_result_ = [model.evals_result_]
self.feature_importances_ = list(model.feature_importances_)
else:
models = []
# Set up progressbar
n_samples = len(df)
progressbar = vaex.utils.progressbars(progress)
column_names = self.features + [self.target]
iterator = df[column_names].to_pandas_df(
chunk_size=self.batch_size)
for i1, i2, chunk in iterator:
progressbar(i1 / n_samples)
data = chunk[self.features].values
target_data = chunk[self.target].values
dtrain = catboost.Pool(data=data,
label=target_data,
**self.pool_params)
model = catboost.train(
params=self.params,
dtrain=dtrain,
num_boost_round=self.num_boost_round,
evals=evals,
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
plot=plot,
**kwargs)
self.evals_result_.append(model.evals_result_)
models.append(model)
progressbar(1.0)
# Weights are key when summing models
if len(self.batch_weights) == 0:
batch_weights = [1 / len(models)] * len(models)
elif self.batch_weights is not None and len(
self.batch_weights) != len(models):
raise ValueError(
"'batch_weights' must be te same length as the number of models."
)
else:
batch_weights = self.batch_weights
# Sum the models
self.booster = catboost.sum_models(
models,
weights=batch_weights,
ctr_merge_policy=self.ctr_merge_policy)
'max_depth': 10,
'num_rounds': 200,
} #score :0.62228 @ 0.01 , split = 0.9, random_state=7
#score :0.62213 @ 0.01 , split = 0.8, random_state=7
#score :0.62113 @ 0.01 , split = 0.7, random_state=7
model_xgb = xgb.train(params_xgb,
d_train_xgb,
105,
watchlist_xgb,
early_stopping_rounds=20,
maximize=True,
verbose_eval=10)
model_lgb = lgb.train(params_lgb, d_train_lgb, 100, valid_sets_lgb)
model_cat = cat.train(pool, params=params_cat, logging_level='Verbose')
p_train_xgb = model_xgb.predict(d_valid_xgb)
p_train_lgb = model_lgb.predict(X_test)
p_train_cat = model_cat.predict(X_test)
p_test_xgb = model_xgb.predict(d_test_xgb)
p_test_lgb = model_lgb.predict(real_test_data)
p_test_cat = model_cat.predict(real_test_data)
final_train = p_train_xgb.reshape(-1, 1)
final_test = p_test_xgb.reshape(-1, 1)
final_train = np.concatenate((final_train, p_train_lgb.reshape(-1, 1)), axis=1)
final_test = np.concatenate((final_test, p_test_lgb.reshape(-1, 1)), axis=1)
final_train = np.concatenate((final_train, p_train_cat.reshape(-1, 1)), axis=1)
final_test = np.concatenate((final_test, p_test_cat.reshape(-1, 1)), axis=1)
def main(options):
print("load train test datasets")
train_all, y_train_all, id_train, test, id_test = pre_train()
cat_params = {
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'learning_rate': options.learning_rate,
'l2_leaf_reg': options.l2_leaf_reg, # L2 regularization coefficient.
'subsample': options.subsample,
'depth': options.depth, # Depth of the tree
'border_count': 255, # The number of splits for numerical features
'thread_count': 6,
'train_dir': 'catboost_train_logs',
'bootstrap_type': 'Bernoulli',
'use_best_model': True,
'random_seed': options.seed
}
roof_flod = options.roof_flod
kf = StratifiedKFold(n_splits=roof_flod,
shuffle=True,
random_state=options.seed)
pred_train_full = np.zeros(train_all.shape[0])
pred_test_full = 0
cv_scores = []
predict_feature = 'catboost_predict_roof_fold{}_lr{}_l2_leaf_reg{}_subsample{}_depth{}_seed{}'.format(
options.roof_flod, options.learning_rate, options.l2_leaf_reg,
options.subsample, options.depth, options.seed)
print('params info:', predict_feature)
for i, (dev_index,
val_index) in enumerate(kf.split(train_all, y_train_all)):
print(
'========== perform fold {}, train size: {}, validate size: {} =========='
.format(i, len(dev_index), len(val_index)))
train_x, val_x = train_all.ix[dev_index], train_all.ix[val_index]
train_y, val_y = y_train_all[dev_index], y_train_all[val_index]
model = cat.train(pool=Pool(train_x, train_y),
params=cat_params,
iterations=460,
eval_set=(val_x, val_y),
verbose=False)
# predict validate
predict_valid = model.predict(val_x.values,
prediction_type='Probability')[:, 1]
valid_auc = evaluate_score(predict_valid, val_y)
# predict test
predict_test = model.predict(test.values,
prediction_type='Probability')[:, 1]
print('valid_auc = {}'.format(valid_auc))
cv_scores.append(valid_auc)
# run-out-of-fold predict
pred_train_full[val_index] = predict_valid
pred_test_full += predict_test
mean_cv_scores = np.mean(cv_scores)
print('Mean cv auc:', mean_cv_scores)
print("saving train predictions for ensemble")
train_pred_df = pd.DataFrame({'userid': id_train})
train_pred_df[predict_feature] = pred_train_full
train_pred_df.to_csv(
"./ensemble/train/catboost/hl_cat_roof{}_predict_train_cv{}_{}.csv".
format(roof_flod, mean_cv_scores, predict_feature),
index=False,
columns=['userid', predict_feature])
print("saving test predictions for ensemble")
pred_test_full = pred_test_full / float(roof_flod)
test_pred_df = pd.DataFrame({'userid': id_test})
test_pred_df[predict_feature] = pred_test_full
test_pred_df.to_csv(
"./ensemble/test/catboost/hl_cat_roof{}_predict_test_cv{}_{}.csv".
format(roof_flod, mean_cv_scores, predict_feature),
index=False,
columns=['userid', predict_feature])
def get_feature_importance(data, target, clf='lightgbm', shuffle=False):
'''
Parameters
----------
data: input dataset, type of dataframe
target: input target dataset, type of series
clf: the name of model want to use, type of string
shuffle: whether to shuffle target dataset (for getting null importance)
Return
------
importance_df: importance of each features, type of dataframe, shape(n_feature, n_importance)
'''
# feature list
train_features = [feature for feature in data.columns.values if feature not in ['target', 'card_id', 'first_active_month']]
categorical_features = [feature for feature in train_features if 'feature_' in feature]
# shuffle the data
y = target.copy().sample(frac=1.0) if shuffle else target.copy()
# using lightgbm
if clf == 'lightgbm':
import lightgbm as lgb
import pandas as pd
# construct training date
train_data = lgb.Dataset(data=data[train_features],\
label=y,\
free_raw_data=False)
# model hyperparameters
lgb_params = {
'num_leaves': 129,
'min_data_in_leaf': 148,
'objective': 'regression',
'max_depth': 9,
'learning_rate': 0.005,
'min_child_samples': 24,
'boosting': 'gbdt',
'feature_fraction': 0.7202,
'bagging_freq': 1,
'bagging_fraction': 0.8125,
'bagging_seed': 11,
'metric': 'rmse',
'lambda_l1': 0.3468,
'random_state': 133,
'verbosity': -1
}
# training the model
clf_lgb = lgb.train(params=lgb_params,\
train_set=train_data,\
num_boost_round=850)
# calculate importance
importance_df = pd.DataFrame()
importance_df['feature'] = list(train_features)
importance_df['importance_gain'] =\
clf_lgb.feature_importance(importance_type='gain')
importance_df['importance_split'] =\
clf_lgb.feature_importance(importance_type='split')
return importance_df
if clf == 'catboost':
from catboost import train, Pool, EFstrType
import pandas as pd
# construct training data
train_data = Pool(data=data[train_features],\
label=y)
# model hyperparameters
cat_params = {
'loss_function': 'RMSE',
'learning_rate': 0.02,
'early_stopping_rounds': 400,
'border_count': 254,
'task_type': 'GPU',
'one_hot_max_size': 6,
'depth': 11,
'l2_leaf_reg': 1.0,
'random_strength': 1.9574,
'bagging_temperature': 20.9049
}
# training the model
clf_cat = train(pool=train_data,\
params=cat_params,\
verbose=False,\
iterations=1000)
# calculate feature importance
importance_df = pd.DataFrame()
importance_df['feature'] = list(train_features)
importance_df['PredictionValuesChange'] =\
clf_cat.get_feature_importance(type='PredictionValuesChange')
return importance_df