kaggle教程--Intermediate Machine Learning
包括以下
- tackle data types often found in real-world datasets (missing values, categorical variables),
- design pipelines to improve the quality of your machine learning code,
- use advanced techniques for model validation (cross-validation),
- build state-of-the-art models that are widely used to win Kaggle competitions (XGBoost), and
- avoid common and important data science mistakes (leakage).
1.deal with missing values
three approaches to dealing with missing values
- A Simple Option: Drop Columns with Missing Values
- A Better Option: Imputation 插值
- An Extension To Imputation
三种方法比较：
# 1 Get names of columns with missing values
cols_with_missing = [col for col in X_train.columns
if X_train[col].isnull().any()]
# Drop columns in training and validation data
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_valid = X_valid.drop(cols_with_missing, axis=1)
print("MAE from Approach 1 (Drop columns with missing values):")
print(score_dataset(reduced_X_train, reduced_X_valid, y_train, y_valid))
from sklearn.impute import SimpleImputer
# 2 Imputation
my_imputer = SimpleImputer()
imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train))
imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))
# Imputation removed column names; put them back
imputed_X_train.columns = X_train.columns
imputed_X_valid.columns = X_valid.columns
print("MAE from Approach 2 (Imputation):")
print(score_dataset(imputed_X_train, imputed_X_valid, y_train, y_valid))
# Make copy to avoid changing original data (when imputing)
X_train_plus = X_train.copy()
X_valid_plus = X_valid.copy()
# Make new columns indicating what will be imputed
for col in cols_with_missing:
X_train_plus[col + '_was_missing'] = X_train_plus[col].isnull()
X_valid_plus[col + '_was_missing'] = X_valid_plus[col].isnull()
# 3 Imputation
my_imputer = SimpleImputer()
imputed_X_train_plus = pd.DataFrame(my_imputer.fit_transform(X_train_plus))
imputed_X_valid_plus = pd.DataFrame(my_imputer.transform(X_valid_plus))
# Imputation removed column names; put them back
imputed_X_train_plus.columns = X_train_plus.columns
imputed_X_valid_plus.columns = X_valid_plus.columns
print("MAE from Approach 3 (An Extension to Imputation):")
print(score_dataset(imputed_X_train_plus, imputed_X_valid_plus, y_train, y_valid))
# Number of missing values in each column of training data
missing_val_count_by_column = (X_train.isnull().sum())
print(missing_val_count_by_column[missing_val_count_by_column > 0])
2.deal with categorical variable
A categorical variable takes only a limited number of values.
three approaches to prepare your categorical data.
- Drop Categorical Variables
- Label Encoding (sklearn包) 可以指定顺序
- One-Hot Encoding One-hot encoding creates new columns indicating the presence (or absence) of each possible value in the original data.
先查看dtypes，一般object类型都是text字符
# Get list of categorical variables
s = (X_train.dtypes == 'object')
object_cols = list(s[s].index)
print("Categorical variables:")
print(object_cols)
# 1 drop category
drop_X_train = X_train.select_dtypes(exclude=['object'])
# label encoding
from sklearn.preprocessing import LabelEncoder
# Make copy to avoid changing original data
label_X_train = X_train.copy()
label_X_valid = X_valid.copy()
# Apply label encoder to each column with categorical data
label_encoder = LabelEncoder()
for col in object_cols:
label_X_train[col] = label_encoder.fit_transform(X_train[col])
label_X_valid[col] = label_encoder.transform(X_valid[col])
# one-hot encoding
from sklearn.preprocessing import OneHotEncoder
# Apply one-hot encoder to each column with categorical data
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols]))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[object_cols]))
# One-hot encoding removed index; put it back
OH_cols_train.index = X_train.index
OH_cols_valid.index = X_valid.index
# Remove categorical columns (will replace with one-hot encoding)
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
# Add one-hot encoded columns to numerical features
OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)
In general, one-hot encoding (Approach 3) will typically perform best, and dropping the categorical columns (Approach 1) typically performs worst, but it varies on a case-by-case basis.
encoding code 训练集和测试集中categories集合相同的是好的列，否则不是
# All categorical columns
object_cols = [col for col in X_train.columns if X_train[col].dtype == "object"]
# Columns that can be safely label encoded
good_label_cols = [col for col in object_cols if
set(X_train[col]) == set(X_valid[col])]
# Problematic columns that will be dropped from the dataset
bad_label_cols = list(set(object_cols)-set(good_label_cols))
查看每一列有几个unique值,one-hot可以用于单一值较少的列，如果单一值较多，编码会产生大量数据。
# Get number of unique entries in each column with categorical data
object_nunique = list(map(lambda col: X_train[col].nunique(), object_cols))
d = dict(zip(object_cols, object_nunique))
# Print number of unique entries by column, in ascending order
sorted(d.items(), key=lambda x: x[1])
3.pipelines
Pipelines are a simple way to keep your data preprocessing and modeling code organized.pipelines have some important benefits
- Cleaner Code
- Fewer Bugs
- Easier to Productionize
- More Options for Model Validation
步骤
- Define Preprocessing Steps
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
# Preprocessing for numerical data
numerical_transformer = SimpleImputer(strategy='constant')
# Preprocessing for categorical data
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='most_frequent')),
('onehot', OneHotEncoder(handle_unknown='ignore'))
])
# Bundle preprocessing for numerical and categorical data
preprocessor = ColumnTransformer(
transformers=[
('num', numerical_transformer, numerical_cols),
('cat', categorical_transformer, categorical_cols)
])
2.Define the Model
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=100, random_state=0)
3.Create and Evaluate the Pipeline
from sklearn.metrics import mean_absolute_error
# Bundle preprocessing and modeling code in a pipeline
my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
('model', model)
])
# Preprocessing of training data, fit model
my_pipeline.fit(X_train, y_train)
# Preprocessing of validation data, get predictions
preds = my_pipeline.predict(X_valid)
4.cross-validation
In cross-validation, we run our modeling process on different subsets of the data to get multiple measures of model quality.
将数据集分成k份，每次取一份做为验证集，剩下做为训练集；每一份都做一次验证集；小数据集可以用cross-validation；如果训练只需数分钟，那么就用cross-validation；
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
my_pipeline = Pipeline(steps=[('preprocessor', SimpleImputer()),
('model', RandomForestRegressor(n_estimators=50,
random_state=0))
])
from sklearn.model_selection import cross_val_score
# Multiply by -1 since sklearn calculates *negative* MAE
scores = -1 * cross_val_score(my_pipeline, X, y,
cv=5,
scoring='neg_mean_absolute_error')
# Select numeric columns only
numeric_cols = [cname for cname in train_data.columns if train_data[cname].dtype in ['int64', 'float64']]
5.XGBoost
learn how to build and optimize models with gradient boosting. This method dominates many Kaggle competitions and achieves state-of-the-art results on a variety of datasets.
Gradient boosting is a method that goes through cycles to iteratively add models into an ensemble.
It begins by initializing the ensemble with a single model, whose predictions can be pretty naive.
Then, we start the cycle:
- First, we use the current ensemble to generate predictions for each observation in the dataset. To make a prediction, we add the predictions from all models in the ensemble.
- These predictions are used to calculate a loss function (like mean squared error, for instance).
- Then, we use the loss function to fit a new model that will be added to the ensemble. Specifically, we determine model parameters so that adding this new model to the ensemble will reduce the loss. (Side note: The “gradient” in “gradient boosting” refers to the fact that we’ll use gradient descent on the loss function to determine the parameters in this new model.)
- Finally, we add the new model to ensemble, and …
- … repeat!
XGBoost stands for extreme gradient boosting
(Scikit-learn has another version of gradient boosting, but XGBoost has some technical advantages.)
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(X_train, y_train)
# validate model
from sklearn.metrics import mean_absolute_error
predictions = my_model.predict(X_valid)
print("Mean Absolute Error: " + str(mean_absolute_error(predictions, y_valid)))
XGBoost has a few parameters that can dramatically affect accuracy and training speed.
迭代次数：n_estimators specifies how many times to go through the modeling cycle described above. It is equal to the number of models that we include in the ensemble. 太小会欠拟合，太大会过拟合。Typical values range from 100-1000, though this depends a lot on the learning_rate .
停止迭代参数：early_stopping_rounds offers a way to automatically find the ideal value for n_estimators. Early stopping causes the model to stop iterating when the validation score stops improving, even if we aren’t at the hard stop for n_estimators. It’s smart to set a high value for n_estimators and then use early_stopping_rounds to find the optimal time to stop iterating. early_stopping_rounds=5 is a reasonable choice. In this case, we stop after 5 straight rounds of deteriorating validation scores.参数在训练时设置 ，连续5次下降时停止迭代。
When using early_stopping_rounds, you also need to set aside some data for calculating the validation scores - this is done by setting the eval_set parameter.
学习速率参数：In general, a small learning rate and large number of estimators will yield more accurate XGBoost models, though it will also take the model longer to train since it does more iterations through the cycle. As default, XGBoost sets learning_rate=0.1.
并行数设置：On larger datasets where runtime is a consideration, you can use parallelism to build your models faster. It’s common to set the parameter n_jobs equal to the number of cores on your machine. On smaller datasets, this won’t help.
my_model = XGBRegressor(n_estimators=1000, learning_rate=0.05, n_jobs=4)
my_model.fit(X_train, y_train,
early_stopping_rounds=5,
eval_set=[(X_valid, y_valid)],
verbose=False)
6.Data leakage
this is one of the most important concepts for practicing data scientists.
Data leakage (or leakage) happens when your training data contains information about the target, but similar data will not be available when the model is used for prediction.
In other words, leakage causes a model to look accurate until you start making decisions with the model, and then the model becomes very inaccurate.
There are two main types of leakage: target leakage and train-test contamination.
- Target leakage occurs when your predictors include data that will not be available at the time you make predictions. It is important to think about target leakage in terms of the timing or chronological order that data becomes available, not merely whether a feature helps make good predictions.
- A different type of leak occurs when you aren’t careful to distinguish training data from validation data.Recall that validation is meant to be a measure of how the model does on data that it hasn’t considered before. You can corrupt this process in subtle ways if the validation data affects the preprocessing behavior. This is sometimes called train-test contamination.