有视频:https://www.youtube.com/watch?v=BFaadIqWlAg

有代码:https://github.com/jem1031/pandas-pipelines-custom-transformers

幼儿级模型


一、模型训练

简单的preprocessing后,仅使用一个“属性”做预测,看看结果如何?

#%%
import pandas as pd
import numpy as np
import os from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.pipeline import Pipeline # SET UP # Read in data
# source: https://data.seattle.gov/Permitting/Special-Events-Permits/dm95-f8w5
data_folder = '../data/'
data_file = 'Special_Events_Permits_2016.csv'
data_file_path = os.path.join(data_folder, data_file)
print("debug: data_file_path is {}".format(data_file_path))
df = pd.read_csv(data_file_path) # Set aside 25% as test data
df_train, df_test = train_test_split(df, random_state=4321) # Take a look
df_train.head() #%%
# SIMPLE MODEL # Binarize string feature
y_train = np.where(df_train.permit_status == 'Complete', 1, 0)
y_test = np.where(df_test.permit_status == 'Complete', 1, 0) print(y_train[:5])
print(y_test[:5]) # Missing value,且只使用这一列做出这次模型训练的特征!
X_train_1 = df_train[['attendance']].fillna(value=0)
X_test_1 = df_test[['attendance']].fillna(value=0) print(X_train_1[:5])
print(X_test_1[:5]) #%%
# Fit model
model_1 = LogisticRegression(random_state=5678)
model_1.fit(X_train_1, y_train)

二、模型评估

评估指标 ROC AUC

(1) 获得二值化的分类结果;

(2) 获得分类的概率数值。

y_pred_train_1 = model_1.predict(X_train_1)
print("y_pred_train_1 is {}".format(y_pred_train_1))
p_pred_train_1 = model_1.predict_proba(X_train_1)[:, 1]
print("p_pred_train_1 is {}".format(p_pred_train_1)) # Evaluate model
# baseline: always predict the average
p_baseline_test = [y_train.mean()]*len(y_test)
auc_baseline = roc_auc_score(y_test, p_baseline_test)
print(auc_baseline) # 0.5

#######################################################
y_pred_test_1 = model_1.predict(X_test_1)
print("y_pred_test_1 is {}".format(y_pred_test_1))
p_pred_test_1 = model_1.predict_proba(X_test_1)[:, 1]
print("p_pred_test_1 is {}".format(p_pred_test_1))

# Evaluate model
auc_test_1 = roc_auc_score(y_test, p_pred_test_1)
print(auc_test_1) # 0.576553672316

Ref: 机器学习评价指标 ROC与AUC 的理解和python实现

以FPR为横坐标,TPR为纵坐标,那么ROC曲线就是改变各种阈值后得到的所有坐标点 (FPR,TPR) 的连线,画出来如下。

红线是随机乱猜情况下的 ROC,曲线越靠左上角,分类器越佳。

AUC(Area Under Curve)就是ROC曲线下的面积。

既然已经这么多评价标准,为什么还要使用ROC和AUC呢?

因为ROC曲线有个很好的特性:当测试集中的正负样本的分布变化的时候,ROC曲线能够保持不变。

评估指标 R2

决定系数R2 Score ,衡量模型预测能力好坏(真实和预测的 相关程度百分比)

预测数据和真实数据越接近,R2越大,当然最大值是 1;模型的R2 值为0,还不如直接用平均值(均值模型)来预测效果好。

Ref: 【从零开始学机器学习12】MSE、RMSE、R2_score

既然不同数据集的量纲不同,很难通过上面的三种方式去比较,那么不妨找一个第三者作为参照,根据参照计算 R方值,就可以比较模型的好坏了。

R2_score < 0 :分子大于分母,训练模型产生的误差比使用均值产生的还要大,也就是训练模型反而不如直接去均值效果好。出现这种情况,通常是模型本身不是线性关系的,而我们误使用了线性模型,导致误差很大。

评估指标 Residual

方差越大,模型越不稳定;

import numpy as np
from sklearn.datasets import load_boston
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as CK
from sklearn.model_selection import cross_val_predict boston = load_boston()
boston_X = boston.data
boston_y = boston.target
train_set = np.random.choice([True, False], len(boston_y),p=[.75, .25])
# 这里获得布尔index,方便从数据集中pick up所需数据 mixed_kernel = kernel = CK(1.0, (1e-4, 1e4)) * RBF(10, (1e-4, 1e4))
gpr = GaussianProcessRegressor(alpha=5, n_restarts_optimizer=20, kernel = mixed_kernel)
gpr.fit(boston_X[train_set], boston_y[train_set])
test_preds = gpr.predict(boston_X[~train_set]
from matplotlib import pyplot as plt
f, ax = plt.subplots(figsize=(10, 7), nrows=3)
f.tight_layout() ax[0].plot(range(len(test_preds)), test_preds, label='Predicted Values')
ax[0].plot(range(len(test_preds)), boston_y[~train_set], label='Actual Values')
ax[0].set_title("Predicted vs Actuals")
# ax[0].legend(loc='best') # 参差图 residual
residual = test_preds - boston_y[~train_set] ax[1].plot(range(len(test_preds)), residual)
ax[1].set_title("Plotted Residuals") ax[2].hist(residual)
ax[2].set_title("Histogram of Residuals")

Result:

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" alt="" width="455" height="326" /> 

模型改进


初探数据

一、数据清理时,需要考虑的内容

Ref: [Pandas] 03 - DataFrame

    • 看看某列,瞧瞧某行【第一步】
    • 可视化一列数据【第一步】
    • 分组统计【第三步】
    • 重采样【第三步】

Ref: [Feature] Preprocessing tutorial

    • 特征统计分布【第一步】
    • 空数据【第二步】
    • 特征间线性关系【第一步】

二、空数据太多怎么办?

可以考虑放弃这个特征。

park_cts = df_train.event_location_park.value_counts(dropna=False)
print(park_cts)
# NaN 364
# Magnuson Park 8
# Gas Works Park 5
# Occidental Park 3
# Greenlake Park 2
# Volunteer Park 2
# Seattle Center 1
# Seward Park 1
# Anchor Park 1
# Madison Park 1
# OTHER 1
# Myrtle Edwards Park 1
# Martin Luther King Jr Memorial Park 1
# Hamilton Viewpoint Park 1
# Ballard Commons Park 1
# Lake Union Park 1
# Judkins Park 1
# Bell Street Park 1
# Comments:
# - about 90% missing values
# - could be new values in test data
# - Note: there are 400+ parks in Seattle

三、数据太多且分散怎么办?

类似高频特征,可分组归类,resampling。

org_cts = df_train.organization.value_counts(dropna=False)
Red Carpet Valet                                             44
Seattle Sounders FC 19
Butler Valet 15
Seafair 9
Fuel Sports Eats and Beats 6
CBS Seattle 5
Pro-Motion Events, Inc. 5
Madison Park Business Association 4
Rejuvenation 4
Fremont Arts Council 4
The U District Partnership 4
Seattle Department of Transportation 4
University of Washington Rowing 4
Upper Left 3
Seattle Symphony 3
Argosy Cruises 3
The Corson Building 3
Waterways Cruises 3
Run for Good Racing Co./5 Focus 3
Seattle Symphony/Benaroya Hall 3
West Seattle Junction Association 3
University of Washington Husky Marching Band 3
Pro-Motion Events, Inc 2
Northwest Yacht Brokers Association 2
Seattle Yacht Club 2
Café Campagne 2
HONK! Fest West 2
Umoja Fest 2
Ethiopians in Seattle 2
Emerald City Pet Rescue 2
..
Fizz Events, LLC 1
Wing Luke Museum of the Asian Pacific American Experience 1
Independent Event Solutions 1
Vulcan Inc. 1
City of Seattle/Animal Shelter 1
GO LONG SR520 Floating Bridge Run 1
The Queen AnneCamber of Commerce 1
Greenwood Knights 1
Alki Art Fair 1
Fizz Events LLC 1
Sea Deli, Inc 1
Rotary Foundation of West Seattle 1
Seattle Buddhist Church 1
TUNE 1
AMERICAN CANCER SOCIETY, INC. 1
CWD Group, Inc. 1
Beacon Arts 1
Southwest Seattle Historical Society 1
Northwest Museum of Legends and Lore 1
magnolia chamber of commerce 1
Ram Racing 1
Seattle Events A Non-Profit Corporation 1
Sound Transit 1
Piranha Blonde Interactive 1
City of Seattle Parks and Recreation Department 1
El Centro de La Raza 1
Northwest Hope and Healing Foundation 1
Orswell Events 1
Lifelong 1
NaN 1
Name: organization, Length: 245, dtype: int64

Result

四、极端值outlier太多怎么办?

”泰尔森估算“是其中的一个策略,但这属于ML estimator的选择范畴。

具体参见:[AI] 深度数学 - Bayes

清理数据

一、特征名字统一格式

# Switch column names to lower_case_with_underscores
def standardize_name(cname):
cname = re.sub(r'[-\.]', ' ', cname)
cname = cname.strip().lower()
cname = re.sub(r'\s+', '_', cname)
return cname print(df_raw.columns)
df_raw.columns = df_raw.columns.map(standardize_name)
print(df_raw.columns)
Index(['Application Date', 'Permit Status', 'Permit Type', 'Event Category',
'Event Sub-Category', 'Name of Event', 'Year-Month-App.',
'Event Start Date', 'Event End Date', 'Event Location - Park',
'Event Location - Neighborhood', 'Council District', 'Precinct',
'Organization', 'Attendance'],
dtype='object')
Index(['application_date', 'permit_status', 'permit_type', 'event_category',
'event_sub_category', 'name_of_event', 'year_month_app',
'event_start_date', 'event_end_date', 'event_location_park',
'event_location_neighborhood', 'council_district', 'precinct',
'organization', 'attendance'],
dtype='object')

Result

二、分割数据

按照时间分割,比较常见的方式。

# Filter to 2016 events
df_raw['event_start_date1'] = pd.to_datetime(df_raw.event_start_date)
df = df_raw[np.logical_and(df_raw.event_start_date1 >= '2016-01-01',
df_raw.event_start_date1 <= '2016-12-31')]
df = df.drop('event_start_date1', axis=1) # Export data
data_file = 'Special_Events_Permits_2016.csv'
df.to_csv(data_folder + data_file, index=False)

特征选择

可以自己添加一些随机特征作为noise,作为特征选择的上手练习。

工作流模型

一、FeatureUnion 组织 Transform

>>> from sklearn.pipeline import FeatureUnion
>>> feature_union = FeatureUnion([
... ('fill_avg', Imputer(strategy='mean')),
... ('fill_mid', Imputer(strategy='median')),
... ('fill_freq', Imputer(strategy='most_frequent'))
... ]) >>> X_train = feature_union.fit_transform(X_train_raw)
>>> X_test = feature_union.transform(X_test_raw)

二、构建自定义 Transform

一个表格中有很多特征,"定性特征" 和 "定量特征" 可以按照如下的思路分开且并行的解决。

# Preprocessing with a Pipeline
pipeline = Pipeline([
('features', DFFeatureUnion([
('categoricals', Pipeline([
('extract', ColumnExtractor(CAT_FEATS)),
('dummy', DummyTransformer())
])),
('numerics', Pipeline([
('extract', ColumnExtractor(NUM_FEATS)),
('zero_fill', ZeroFillTransformer()),
('log', Log1pTransformer())
]))
])),
('scale', DFStandardScaler())
])

固定的套路是:继承TransformerMixin后,实现 fit 和 tranform 方法。

class DummyTransformer(TransformerMixin):

    def __init__(self):
self.dv = None def fit(self, X, y=None):
# assumes all columns of X are strings
Xdict = X.to_dict('records')
self.dv = DictVectorizer(sparse=False)
self.dv.fit(Xdict)
return self def transform(self, X):
# assumes X is a DataFrame
Xdict = X.to_dict('records')
Xt = self.dv.transform(Xdict)
cols = self.dv.get_feature_names()
Xdum = pd.DataFrame(Xt, index=X.index, columns=cols)
# drop column indicating NaNs
nan_cols = [c for c in cols if '=' not in c]
Xdum = Xdum.drop(nan_cols, axis=1)
return Xdum

知识点

处理 "定性特征" 的套路。

Ref: pandas.DataFrame.to_dict()的使用详解

Ref: 特征提升之特征抽取----DictVectorizer

三、特征联合 Feature Union

因为默认是用numpy作为参数格式,但这里都是dataframe结构,稍微自定义下即可。

class DFFeatureUnion(TransformerMixin):
# FeatureUnion but for pandas DataFrames def __init__(self, transformer_list):
self.transformer_list = transformer_list def fit(self, X, y=None):
# 执行完,却不需要结果
for (_, t) in self.transformer_list:
t.fit(X, y)
return self def transform(self, X):
# 执行完,需要结果;因为结果还要被用来做reduce操作
Xts = [t.transform(X) for _, t in self.transformer_list]
Xunion = reduce(lambda X1, X2: pd.merge(X1, X2, left_index=True, right_index=True), Xts)
return Xunion

四、训练模型并测试

可见,测试结果好了一些。

pipeline.fit(df_train)
X_train_2 = pipeline.transform(df_train)
X_test_2 = pipeline.transform(df_test) # Fit model
model_2 = LogisticRegression(random_state=5678)
model_2.fit(X_train_2, y_train)
y_pred_train_2 = model_2.predict(X_train_2)
p_pred_train_2 = model_2.predict_proba(X_train_2)[:, 1] # Evaluate model
p_pred_test_2 = model_2.predict_proba(X_test_2)[:, 1]
auc_test_2 = roc_auc_score(y_test, p_pred_test_2)
print(auc_test_2) # 0.70508474576

过拟合

更多的特征导致过拟合,如下,反而性能下降了。

# Preprocessing with a Pipeline
pipeline3 = Pipeline([
('features', DFFeatureUnion([
('dates', Pipeline([
('extract', ColumnExtractor(DATE_FEATS)),  # 考虑日期相关特征  
('to_date', DateFormatter()),
('diffs', DateDiffer()),
('mid_fill', DFImputer(strategy='median'))
])),
('categoricals', Pipeline([
('extract', ColumnExtractor(CAT_FEATS)),
('dummy', DummyTransformer())
])),
('multi_labels', Pipeline([
('extract', ColumnExtractor(MULTI_FEATS)),
('multi_dummy', MultiEncoder(sep=';'))
])),
('numerics', Pipeline([
('extract', ColumnExtractor(NUM_FEATS)),
('zero_fill', ZeroFillTransformer()),
('log', Log1pTransformer())
]))
])),
('scale', DFStandardScaler())
])
pipeline3.fit(df_train)
X_train_3 = pipeline3.transform(df_train)
X_test_3 = pipeline3.transform(df_test) # Fit model
model_3 = LogisticRegression(random_state=5678)
model_3.fit(X_train_3, y_train)
y_pred_train_3 = model_3.predict(X_train_3)
p_pred_train_3 = model_3.predict_proba(X_train_3)[:, 1] # Evaluate model
p_pred_test_3 = model_3.predict_proba(X_test_3)[:, 1]
auc_test_3 = roc_auc_score(y_test, p_pred_test_3)
print(auc_test_3) # 0.680790960452 # too many features -> starting to overfit

End.

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