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exemplo.py
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import numpy as np
import pandas as pd
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from numba import jit
import time
import gc
MAX_ROUNDS = 400
OPTIMIZE_ROUNDS = False
LEARNING_RATE = 0.07
EARLY_STOPPING_ROUNDS = 50
# Note: I set EARLY_STOPPING_ROUNDS high so that (when OPTIMIZE_ROUNDS is set)
# I will get lots of information to make my own judgment. You should probably
# reduce EARLY_STOPPING_ROUNDS if you want to do actual early stopping.
# from CPMP's kernel https://www.kaggle.com/cpmpml/extremely-fast-gini-computation
@jit
def eval_gini(y_true, y_prob):
y_true = np.asarray(y_true)
y_true = y_true[np.argsort(y_prob)]
ntrue = 0
gini = 0
delta = 0
n = len(y_true)
for i in range(n-1, -1, -1):
y_i = y_true[i]
ntrue += y_i
gini += y_i * delta
delta += 1 - y_i
gini = 1 - 2 * gini / (ntrue * (n - ntrue))
return gini
def gini_xgb(preds, dtrain):
labels = dtrain.get_label()
gini_score = -eval_gini(labels, preds)
return [('gini', gini_score)]
def add_noise(series, noise_level):
return series * (1 + noise_level * np.random.randn(len(series)))
def target_encode(trn_series=None, # Revised to encode validation series
val_series=None,
tst_series=None,
target=None,
min_samples_leaf=1,
smoothing=1,
noise_level=0):
"""
Smoothing is computed like in the following paper by Daniele Micci-Barreca
https://kaggle2.blob.core.windows.net/forum-message-attachments/225952/7441/high%20cardinality%20categoricals.pdf
trn_series : training categorical feature as a pd.Series
tst_series : test categorical feature as a pd.Series
target : target data as a pd.Series
min_samples_leaf (int) : minimum samples to take category average into account
smoothing (int) : smoothing effect to balance categorical average vs prior
"""
assert len(trn_series) == len(target)
assert trn_series.name == tst_series.name
temp = pd.concat([trn_series, target], axis=1)
# Compute target mean
averages = temp.groupby(by=trn_series.name)[target.name].agg(["mean", "count"])
# Compute smoothing
smoothing = 1 / (1 + np.exp(-(averages["count"] - min_samples_leaf) / smoothing))
# Apply average function to all target data
prior = target.mean()
# The bigger the count the less full_avg is taken into account
averages[target.name] = prior * (1 - smoothing) + averages["mean"] * smoothing
averages.drop(["mean", "count"], axis=1, inplace=True)
# Apply averages to trn and tst series
ft_trn_series = pd.merge(
trn_series.to_frame(trn_series.name),
averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),
on=trn_series.name,
how='left')['average'].rename(trn_series.name + '_mean').fillna(prior)
# pd.merge does not keep the index so restore it
ft_trn_series.index = trn_series.index
ft_val_series = pd.merge(
val_series.to_frame(val_series.name),
averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),
on=val_series.name,
how='left')['average'].rename(trn_series.name + '_mean').fillna(prior)
# pd.merge does not keep the index so restore it
ft_val_series.index = val_series.index
ft_tst_series = pd.merge(
tst_series.to_frame(tst_series.name),
averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),
on=tst_series.name,
how='left')['average'].rename(trn_series.name + '_mean').fillna(prior)
# pd.merge does not keep the index so restore it
ft_tst_series.index = tst_series.index
return add_noise(ft_trn_series, noise_level), add_noise(ft_val_series, noise_level), add_noise(ft_tst_series, noise_level)
# Read data
train_df = pd.read_csv('train.csv', na_values="-1") # .iloc[0:200,:]
test_df = pd.read_csv('test.csv', na_values="-1")
# from olivier
train_features = [
"ps_car_13", # : 1571.65 / shadow 609.23
"ps_reg_03", # : 1408.42 / shadow 511.15
"ps_ind_05_cat", # : 1387.87 / shadow 84.72
"ps_ind_03", # : 1219.47 / shadow 230.55
"ps_ind_15", # : 922.18 / shadow 242.00
"ps_reg_02", # : 920.65 / shadow 267.50
"ps_car_14", # : 798.48 / shadow 549.58
"ps_car_12", # : 731.93 / shadow 293.62
"ps_car_01_cat", # : 698.07 / shadow 178.72
"ps_car_07_cat", # : 694.53 / shadow 36.35
"ps_ind_17_bin", # : 620.77 / shadow 23.15
"ps_car_03_cat", # : 611.73 / shadow 50.67
"ps_reg_01", # : 598.60 / shadow 178.57
"ps_car_15", # : 593.35 / shadow 226.43
"ps_ind_01", # : 547.32 / shadow 154.58
"ps_ind_16_bin", # : 475.37 / shadow 34.17
"ps_ind_07_bin", # : 435.28 / shadow 28.92
"ps_car_06_cat", # : 398.02 / shadow 212.43
"ps_car_04_cat", # : 376.87 / shadow 76.98
"ps_ind_06_bin", # : 370.97 / shadow 36.13
"ps_car_09_cat", # : 214.12 / shadow 81.38
"ps_car_02_cat", # : 203.03 / shadow 26.67
"ps_ind_02_cat", # : 189.47 / shadow 65.68
"ps_car_11", # : 173.28 / shadow 76.45
"ps_car_05_cat", # : 172.75 / shadow 62.92
"ps_calc_09", # : 169.13 / shadow 129.72
"ps_calc_05", # : 148.83 / shadow 120.68
"ps_ind_08_bin", # : 140.73 / shadow 27.63
"ps_car_08_cat", # : 120.87 / shadow 28.82
"ps_ind_09_bin", # : 113.92 / shadow 27.05
"ps_ind_04_cat", # : 107.27 / shadow 37.43
"ps_ind_18_bin", # : 77.42 / shadow 25.97
"ps_ind_12_bin", # : 39.67 / shadow 15.52
"ps_ind_14", # : 37.37 / shadow 16.65
]
# add combinations
combs = [
('ps_reg_01', 'ps_car_02_cat'),
('ps_reg_01', 'ps_car_04_cat'),
]
# Process data
id_test = test_df['id'].values
id_train = train_df['id'].values
y = train_df['target']
start = time.time()
for n_c, (f1, f2) in enumerate(combs):
name1 = f1 + "_plus_" + f2
print('current feature %60s %4d in %5.1f'.format(name1, n_c + 1, (time.time() - start) / 60))
print('\r' * 75)
train_df[name1] = train_df[f1].apply(lambda x: str(x)) + "_" + train_df[f2].apply(lambda x: str(x))
test_df[name1] = test_df[f1].apply(lambda x: str(x)) + "_" + test_df[f2].apply(lambda x: str(x))
# Label Encode
lbl = LabelEncoder()
lbl.fit(list(train_df[name1].values) + list(test_df[name1].values))
train_df[name1] = lbl.transform(list(train_df[name1].values))
test_df[name1] = lbl.transform(list(test_df[name1].values))
train_features.append(name1)
X = train_df[train_features]
test_df = test_df[train_features]
f_cats = [f for f in X.columns if "_cat" in f]
y_valid_pred = 0*y
y_test_pred = 0
# Set up folds
K = 5
kf = KFold(n_splits = K, random_state = 1, shuffle = True)
np.random.seed(0)
# Set up classifier
model = XGBClassifier(
n_estimators=MAX_ROUNDS,
max_depth=4,
objective="binary:logistic",
learning_rate=LEARNING_RATE,
subsample=.8,
min_child_weight=6,
colsample_bytree=.8,
scale_pos_weight=1.6,
gamma=10,
reg_alpha=8,
reg_lambda=1.3,
)
# Run CV
for i, (train_index, test_index) in enumerate(kf.split(train_df)):
# Create data for this fold
y_train, y_valid = y.iloc[train_index].copy(), y.iloc[test_index]
X_train, X_valid = X.iloc[train_index,:].copy(), X.iloc[test_index,:].copy()
X_test = test_df.copy()
print( "\nFold ", i)
# Enocode data
for f in f_cats:
X_train[f + "_avg"], X_valid[f + "_avg"], X_test[f + "_avg"] = target_encode(
trn_series=X_train[f],
val_series=X_valid[f],
tst_series=X_test[f],
target=y_train,
min_samples_leaf=200,
smoothing=10,
noise_level=0
)
# Run model for this fold
if OPTIMIZE_ROUNDS:
eval_set=[(X_valid,y_valid)]
fit_model = model.fit( X_train, y_train,
eval_set=eval_set,
eval_metric=gini_xgb,
early_stopping_rounds=EARLY_STOPPING_ROUNDS,
verbose=False
)
print( " Best N trees = ", model.best_ntree_limit )
print( " Best gini = ", model.best_score )
else:
fit_model = model.fit( X_train, y_train )
# Generate validation predictions for this fold
pred = fit_model.predict_proba(X_valid)[:,1]
print( eval_gini(y_valid, pred) )
y_valid_pred.iloc[test_index] = pred
# Accumulate test set predictions
y_test_pred += fit_model.predict_proba(X_test)[:,1]
del X_test, X_train, X_valid, y_train
y_test_pred /= K # Average test set predictions
print( "\nGini for full training set:" )
eval_gini(y, y_valid_pred)
# Save validation predictions for stacking/ensembling
val = pd.DataFrame()
val['id'] = id_train
val['target'] = y_valid_pred.values
val.to_csv('xgb_valid.csv', float_format='%.6f', index=False)
# Create submission file
sub = pd.DataFrame()
sub['id'] = id_test
sub['target'] = y_test_pred
sub.to_csv('xgb_submit.csv', float_format='%.6f', index=False)