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1. 簡介
內心一直想把自己前一段時間寫的代碼整理一下，梳理一下知識點，方便以後查看，同時也方便和大家交流。希望我的分享能幫助到一些小白用戶快速前進，也希望大家看到不足之處慷慨的指出，相互學習，快速成長。我將從三個部分介紹數據挖掘類比賽中常用的一些方法，分別是lightgbm、xgboost和keras實現的mlp模型，分別介紹他們實現的二分類任務、多分類任務和迴歸任務，並給出完整的開源python代碼。這篇文章主要介紹基於lightgbm實現的三類任務。源碼地址：https://github.com/QLMX/data_mining_models
2.數據加載
該部分數據是基於拍拍貸比賽截取的一部分特徵，隨機選擇了5000個訓練數據，3000個測試數據。針對其中gender、cell_province等類別特徵，直接進行重新編碼處理。原始數據的lable是0-32，共有33個類別的數據。針對二分類任務，將原始label爲32的數據直接轉化爲1，label爲其他的數據轉爲0；迴歸問題就是將這些類別作爲待預測的目標值。代碼如下：其中gc是釋放不必要的內存。
## category feature one_hot
test_data['label'] = -1
data = pd.concat([train_data, test_data])
cate_feature = ['gender', 'cell_province', 'id_province', 'id_city', 'rate', 'term']
for item in cate_feature:
data[item] = LabelEncoder().fit_transform(data[item])
train = data[data['label'] != -1]
test = data[data['label'] == -1]
## Clean up the memory
del data, train_data, test_data
gc.collect()
## get train feature
del_feature = ['auditing_date', 'due_date', 'label']
features = [i for i in train.columns if i not in del_feature]
## Convert the label to two categories
train['label'] = train['label'].apply(lambda x: 1 if x==32 else 0)
train_x = train[features]
train_y = train['label'].values
test = test[features]
3.二分類任務
params = {'num_leaves': 60, #結果對最終效果影響較大，越大值越好，太大會出現過擬合
'min_data_in_leaf': 30,
'objective': 'binary', #定義的目標函數
'max_depth': -1,
'learning_rate': 0.03,
"min_sum_hessian_in_leaf": 6,
"boosting": "gbdt",
"feature_fraction": 0.9, #提取的特徵比率
"bagging_freq": 1,
"bagging_fraction": 0.8,
"bagging_seed": 11,
"lambda_l1": 0.1, #l1正則
# 'lambda_l2': 0.001, #l2正則
"verbosity": -1,
"nthread": -1, #線程數量，-1表示全部線程，線程越多，運行的速度越快
'metric': {'binary_logloss', 'auc'}, ##評價函數選擇
"random_state": 2019, #隨機數種子，可以防止每次運行的結果不一致
# 'device': 'gpu' ##如果安裝的事gpu版本的lightgbm,可以加快運算
}
folds = KFold(n_splits=5, shuffle=True, random_state=2019)
prob_oof = np.zeros((train_x.shape[0], ))
test_pred_prob = np.zeros((test.shape[0], ))
## train and predict
feature_importance_df = pd.DataFrame()
for fold_, (trn_idx, val_idx) in enumerate(folds.split(train)):
print("fold {}".format(fold_ + 1))
trn_data = lgb.Dataset(train_x.iloc[trn_idx], label=train_y[trn_idx])
val_data = lgb.Dataset(train_x.iloc[val_idx], label=train_y[val_idx])
clf = lgb.train(params,
trn_data,
num_round,
valid_sets=[trn_data, val_data],
verbose_eval=20,
early_stopping_rounds=60)
prob_oof[val_idx] = clf.predict(train_x.iloc[val_idx], num_iteration=clf.best_iteration)
fold_importance_df = pd.DataFrame()
fold_importance_df["Feature"] = features
fold_importance_df["importance"] = clf.feature_importance()
fold_importance_df["fold"] = fold_ + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
test_pred_prob += clf.predict(test[features], num_iteration=clf.best_iteration) / folds.n_splits
threshold = 0.5
for pred in test_pred_prob:
result = 1 if pred > threshold else 0
上面的參數中目標函數採用的事 binary，評價函數採用的是 {'binary_logloss','auc'}，可以根據需要對評價函數做調整，可以設定一個或者多個評價函數；'num_leaves'對最終的結果影響較大，如果值設置的過大會出現過擬合現象。
針對模型訓練部分，採用的事5折交叉訓練的方法，常用的5折統計有兩種：StratifiedKFold和KFold，其中最大的不同是StratifiedKFold分層採樣交叉切分，確保訓練集，測試集中各類別樣本的比例與原始數據集中相同，實際使用中可以根據具體的數據分別測試兩者的表現。
最後 fold_importance_df表存放的事模型的特徵重要性，可以方便分析特徵重要性
4.多分類任務
params = {'num_leaves': 60,
'min_data_in_leaf': 30,
'objective': 'multiclass',
'num_class': 33,
'max_depth': -1,
'learning_rate': 0.03,
"min_sum_hessian_in_leaf": 6,
"boosting": "gbdt",
"feature_fraction": 0.9,
"bagging_freq": 1,
"bagging_fraction": 0.8,
"bagging_seed": 11,
"lambda_l1": 0.1,
"verbosity": -1,
"nthread": 15,
'metric': 'multi_logloss',
"random_state": 2019,
# 'device': 'gpu'
}
folds = KFold(n_splits=5, shuffle=True, random_state=2019)
prob_oof = np.zeros((train_x.shape[0], 33))
test_pred_prob = np.zeros((test.shape[0], 33))
## train and predict
feature_importance_df = pd.DataFrame()
for fold_, (trn_idx, val_idx) in enumerate(folds.split(train)):
print("fold {}".format(fold_ + 1))
trn_data = lgb.Dataset(train_x.iloc[trn_idx], label=train_y.iloc[trn_idx])
val_data = lgb.Dataset(train_x.iloc[val_idx], label=train_y.iloc[val_idx])
clf = lgb.train(params,
trn_data,
num_round,
valid_sets=[trn_data, val_data],
verbose_eval=20,
early_stopping_rounds=60)
prob_oof[val_idx] = clf.predict(train_x.iloc[val_idx], num_iteration=clf.best_iteration)
fold_importance_df = pd.DataFrame()
fold_importance_df["Feature"] = features
fold_importance_df["importance"] = clf.feature_importance()
fold_importance_df["fold"] = fold_ + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
test_pred_prob += clf.predict(test[features], num_iteration=clf.best_iteration) / folds.n_splits
result = np.argmax(test_pred_prob, axis=1)
該部分同上面最大的區別就是該表了損失函數和評價函數。分別更換爲 'multiclass'和 'multi_logloss'，當進行多分類任務是必須還要指定類別數：'num_class'。
5.迴歸任務
params = {'num_leaves': 38,
'min_data_in_leaf': 50,
'objective': 'regression',
'max_depth': -1,
'learning_rate': 0.02,
"min_sum_hessian_in_leaf": 6,
"boosting": "gbdt",
"feature_fraction": 0.9,
"bagging_freq": 1,
"bagging_fraction": 0.7,
"bagging_seed": 11,
"lambda_l1": 0.1,
"verbosity": -1,
"nthread": 4,
'metric': 'mae',
"random_state": 2019,
# 'device': 'gpu'
}
def mean_absolute_percentage_error(y_true, y_pred):
return np.mean(np.abs((y_true - y_pred) / (y_true))) * 100
def smape_func(preds, dtrain):
label = dtrain.get_label().values
epsilon = 0.1
summ = np.maximum(0.5 + epsilon, np.abs(label) + np.abs(preds) + epsilon)
smape = np.mean(np.abs(label - preds) / summ) * 2
return 'smape', float(smape), False
folds = KFold(n_splits=5, shuffle=True, random_state=2019)
oof = np.zeros(train_x.shape[0])
predictions = np.zeros(test.shape[0])
train_y = np.log1p(train_y) # Data smoothing
feature_importance_df = pd.DataFrame()
for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_x)):
print("fold {}".format(fold_ + 1))
trn_data = lgb.Dataset(train_x.iloc[trn_idx], label=train_y.iloc[trn_idx])
val_data = lgb.Dataset(train_x.iloc[val_idx], label=train_y.iloc[val_idx])
clf = lgb.train(params,
trn_data,
num_round,
valid_sets=[trn_data, val_data],
verbose_eval=200,
early_stopping_rounds=200)
oof[val_idx] = clf.predict(train_x.iloc[val_idx], num_iteration=clf.best_iteration)
fold_importance_df = pd.DataFrame()
fold_importance_df["Feature"] = features
fold_importance_df["importance"] = clf.feature_importance()
fold_importance_df["fold"] = fold_ + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
predictions += clf.predict(test, num_iteration=clf.best_iteration) / folds.n_splits
print('mse %.6f' % mean_squared_error(train_y, oof))
print('mae %.6f' % mean_absolute_error(train_y, oof))
result = np.expm1(predictions) #reduction
result = predictions
在迴歸任務中對目標函數值添加了一個log平滑，如果待預測的結果值跨度很大，做log平滑很有很好的效果提升。
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