【Kaggle】Intermediate Machine Learning（缺失值+文字特征处理）

文章目录

1. Introduction
2. Missing Values 缺失值处理
3. Categorical Variables 文字变量处理

from https://www.kaggle.com/learn/intermediate-machine-learning

下一篇：【Kaggle】Intermediate Machine Learning（管道+交叉验证）

1. Introduction

按照教程给的7个特征，给定5种参数下的随机森林模型，选出mae误差最小的，进行提交

import pandas as pd
from sklearn.model_selection import train_test_split# Read the data
X_full = pd.read_csv('../input/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/test.csv', index_col='Id')# Obtain target and predictors
y = X_full.SalePrice
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = X_full[features].copy()
X_test = X_test_full[features].copy()# Break off validation set from training data
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2,random_state=0)
from sklearn.ensemble import RandomForestRegressor# Define the models，定义了5种参数的随机森林模型
model_1 = RandomForestRegressor(n_estimators=50, random_state=0)
model_2 = RandomForestRegressor(n_estimators=100, random_state=0)
model_3 = RandomForestRegressor(n_estimators=100, criterion='mae', random_state=0)
model_4 = RandomForestRegressor(n_estimators=200, min_samples_split=20, random_state=0)
model_5 = RandomForestRegressor(n_estimators=100, max_depth=7, random_state=0)models = [model_1, model_2, model_3, model_4, model_5]from sklearn.metrics import mean_absolute_error# Function for comparing different models
def score_model(model, X_t=X_train, X_v=X_valid, y_t=y_train, y_v=y_valid):model.fit(X_t, y_t)preds = model.predict(X_v)return mean_absolute_error(y_v, preds)
# 找出误差最小的模型
for i in range(0, len(models)):mae = score_model(models[i])print("Model %d MAE: %d" % (i+1, mae))best_model = models[2]
my_model = best_modelmy_model.fit(X, y)
# Generate test predictions
preds_test = my_model.predict(X_test)# Save predictions in format used for competition scoring
output = pd.DataFrame({'Id': X_test.index,'SalePrice': preds_test})
output.to_csv('submission.csv', index=False)

评分：mae误差 20998.83780

2. Missing Values 缺失值处理

缺失值的处理：

丢弃整列，缺点是信息丢失严重

cols_with_missing = [col for col in X_train.columnsif X_train[col].isnull().any()] # Your code here# Fill in the lines below: 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)

差值填补，比如填充均值等

from sklearn.impute import SimpleImputer# Fill in the lines below: imputation
help(SimpleImputer)
imp = SimpleImputer()# 默认以均值进行填补
# imp = SimpleImputer(strategy="median") # 中位数填补
imputed_X_train = pd.DataFrame(imp.fit_transform(X_train))# 拟合，填补
imputed_X_valid = pd.DataFrame(imp.transform(X_valid))#填补# Fill in the lines below: imputation removed column names; put them back
imputed_X_train.columns = X_train.columns # 差值去除了特征名称，再填上
imputed_X_valid.columns = X_valid.columns

SimpleImputer 参考如下

class SimpleImputer(_BaseImputer)|  SimpleImputer(missing_values=nan, strategy='mean', fill_value=None,verbose=0, copy=True, add_indicator=False)|  |  Imputation transformer for completing missing values.|  |  Read more in the :ref:`User Guide <impute>`.|  |  Parameters|  ----------|  missing_values : number, string, np.nan (default) or None|      The placeholder for the missing values. All occurrences of|      `missing_values` will be imputed.|  |  strategy : string, default='mean'|      The imputation strategy.|  |      - If "mean", then replace missing values using the mean along|        each column. Can only be used with numeric data.|      - If "median", then replace missing values using the median along|        each column. Can only be used with numeric data.|      - If "most_frequent", then replace missing using the most frequent|        value along each column. Can be used with strings or numeric data.|      - If "constant", then replace missing values with fill_value. Can be|        used with strings or numeric data.

评分：mae误差 16619.07644

3. Categorical Variables 文字变量处理

分类变量处理方法：

直接丢弃，如果没有用的话
Label Encoding 标记编码：比如频率：“Never” (0) < “Rarely” (1) < “Most days” (2) < “Every day” (3)，将字符串分类成几类，用数字表示，特征存在内在顺序 (ordinal feature)
One-Hot Encoding，特征无内在顺序，会在数据里新生成一系列的列，一般来说最后一种效果最好，但是特征中值的种类过多的话，该方法会把数据集扩的比较大

# Get list of categorical variables，获取非数字类变量
s = (X_train.dtypes == 'object')
object_cols = list(s[s].index)print("Categorical variables:")
print(object_cols)

Categorical variables:
['Type', 'Method', 'Regionname'] # 特征名称

直接丢弃

drop_X_train = X_train.select_dtypes(exclude=['object'])
drop_X_valid = X_valid.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

# 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，放回idx
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) # 丢弃原有的文字列，只剩数字
num_X_valid = X_valid.drop(object_cols, axis=1)# Add one-hot encoded columns to numerical features # 数字列和编码后的文本特征列合并
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)

遇见训练集和测试集的文字变量种类不一样：

检查哪些特征在两个集合里都是一样的，不一样的话直接编码会出错

# 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))

这里处理的方法是，丢弃不一致的，对一致的进行编码转换

from sklearn.preprocessing import LabelEncoder# Drop categorical columns that will not be encoded
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)# Apply label encoder
labEncoder = LabelEncoder()
for feature in set(good_label_cols):label_X_train[feature] = labEncoder.fit_transform(label_X_train[feature])label_X_valid[feature] = labEncoder.transform(label_X_valid[feature])

查看文字特征里，有多少种变量值

# 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])

[('Street', 2), # 街道有2个不同的值('Utilities', 2),('CentralAir', 2),。。。('Exterior2nd', 16),('Neighborhood', 25)] # 种数较多的不宜用one-hot，# 数据集扩大的很厉害，可以label-encoding，或丢弃

# Columns that will be one-hot encoded
# 不同数值数 < 10 的特征进行 one-hot编码
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]# Columns that will be dropped from the dataset
# 剩余的（两个set做差），丢弃
high_cardinality_cols = list(set(object_cols)-set(low_cardinality_cols))

from sklearn.preprocessing import OneHotEncoder# one_hot编码器
ohEnc = OneHotEncoder(handle_unknown='ignore', sparse=False)# 不同数值数 < 10 的特征one_hot编码
OH_X_train = pd.DataFrame(ohEnc.fit_transform(X_train[low_cardinality_cols]))
OH_X_valid = pd.DataFrame(ohEnc.transform(X_valid[low_cardinality_cols]))# 编码后index丢失，再加上
OH_X_train.index = X_train.index
OH_X_valid.index = X_valid.index# 数字特征（原数据丢弃文字特征，即得到）
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)# 合并 数字特征 + one_hot编码（记得恢复index）后的文字特征（特征数值种类多的丢弃了）
OH_X_train = pd.concat([OH_X_train, num_X_train], axis=1)
OH_X_valid = pd.concat([OH_X_valid, num_X_valid], axis=1)