Python 机器学习

from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

# 假设您已将 'pima-indians-diabetes.csv' 文件保存在本地路径
path = r"C:\pima-indians-diabetes.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age',
               'class']
data = read_csv(path, names=headernames)
array = data.values
X = array[:,0:8] # 特征
Y = array[:,8]   # 目标

estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('lda', LinearDiscriminantAnalysis()))
model = Pipeline(estimators)

# 修正 KFold 的 random_state 参数以确保可重复性
kfold = KFold(n_splits=20, random_state=7, shuffle=True) # 添加 shuffle=True 以确保 KFold 的随机性

results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())

0.7790148448043184

from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest

# 假设您已将 'pima-indians-diabetes.csv' 文件保存在本地路径
path = r"C:\pima-indians-diabetes.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age',
               'class']
data = read_csv(path, names=headernames)
array = data.values
X = array[:,0:8] # 特征
Y = array[:,8]   # 目标

features = []
features.append(('pca', PCA(n_components=3)))
features.append(('select_best', SelectKBest(k=6)))
feature_union = FeatureUnion(features)

estimators = []
estimators.append(('feature_union', feature_union))
estimators.append(('logistic', LogisticRegression(solver='liblinear'))) # 添加 solver 参数，因为默认值已更改
model = Pipeline(estimators)

# 修正 KFold 的 random_state 参数以确保可重复性
kfold = KFold(n_splits=20, random_state=7, shuffle=True) # 添加 shuffle=True 以确保 KFold 的随机性

results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())

0.7789811066126855

23. Machine Learning with Pipelines – Machine
 Automatic
 Learning with Workflows
 Python
Introduction
In order to execute and produce results successfully, a machine learning model must
automate some standard workflows. The process of automate these standard workflows
can be done with the help of Scikit-learn Pipelines. From a data scientist’s perspective,
pipeline is a generalized, but very important concept. It basically allows data flow from its
raw format to some useful information. The working of pipelines can be understood with
the help of following diagram:
Data
 Data
 ML Model
 Model
 Deployment
Preparation
 Training
 Evaluation
Ingestion
ML Model
Re-training
The blocks of ML pipelines are as follows:
Data ingestion: As the name suggests, it is the process of importing the data for use in
ML project. The data can be extracted in real time or batches from single or multiple
systems. It is one of the most challenging steps because the quality of data can affect the
whole ML model.
Data Preparation: After importing the data, we need to prepare data to be used for our
ML model. Data preprocessing is one of the most important technique of data preparation.
ML Model Training: Next step is to train our ML model. We have various ML algorithms
like supervised, unsupervised, reinforcement to extract the features from data, and make
predictions.
Model Evaluation: Next, we need to evaluate the ML model. In case of AutoML pipeline,
ML model can be evaluated with the help of various statistical methods and business rules.
ML Model retraining: In case of AutoML pipeline, it is not necessary that the first model
is best one. The first model is considered as a baseline model and we can train it repeatably
to increase model’s accuracy.
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Deployment: At last, we need to deploy the model. This step involves applying and
migrating the model to business operations for their use.
Challenges Accompanying ML Pipelines
In order to create ML pipelines, data scientists face many challenges. These challenges fall
into the following three categories:
Quality of Data
The success of any ML model depends heavily on the quality of data. If the data we are
providing to ML model is not accurate, reliable and robust, then we are going to end with
wrong or misleading output.
Data Reliability
Another challenge associated with ML pipelines is the reliability of data we are providing
to the ML model. As we know, there can be various sources from which data scientist can
acquire data but to get the best results, it must be assured that the data sources are
reliable and trusted.
Data Accessibility
To get the best results out of ML pipelines, the data itself must be accessible which requires
consolidation, cleansing and curation of data. As a result of data accessibility property,
metadata will be updated with new tags.
Modelling ML Pipeline and Data Preparation
Data leakage, happening from training dataset to testing dataset, is an important issue
for data scientist to deal with while preparing data for ML model. Generally, at the time of
data preparation, data scientist uses techniques like standardization or normalization on
entire dataset before learning. But these techniques cannot help us from the leakage of
data because the training dataset would have been influenced by the scale of the data in
the testing dataset.
By using ML pipelines, we can prevent this data leakage because pipelines ensure that
data preparation like standardization is constrained to each fold of our cross-validation
procedure.
Example
The following is an example in Python that demonstrate data preparation and model
evaluation workflow. For this purpose, we are using Pima Indian Diabetes dataset from
Sklearn. First, we will be creating pipeline that standardized the data. Then a Linear
Discriminative analysis model will be created and at last the pipeline will be evaluated
using 10-fold cross validation.
First, import the required packages as follows:
from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
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from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
Now, we need to load the Pima diabetes dataset as did in previous examples:
path = r"C:\pima-indians-diabetes.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age',
'class']
data = read_csv(path, names=headernames)
array = data.values
Next, we will create a pipeline with the help of the following code:
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('lda', LinearDiscriminantAnalysis()))
model = Pipeline(estimators)
At last, we are going to evaluate this pipeline and output its accuracy as follows:
kfold = KFold(n_splits=20, random_state=7)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())
Output
0.7790148448043184
The above output is the summary of accuracy of the setup on the dataset.
Modelling ML Pipeline and Feature Extraction
Data leakage can also happen at feature extraction step of ML model. That is why feature
extraction procedures should also be restricted to stop data leakage in our training dataset.
As in the case of data preparation, by using ML pipelines, we can prevent this data leakage
also. FeatureUnion, a tool provided by ML pipelines can be used for this purpose.
Example
The following is an example in Python that demonstrates feature extraction and model
evaluation workflow. For this purpose, we are using Pima Indian Diabetes dataset from
Sklearn.
First, 3 features will be extracted with PCA (Principal Component Analysis). Then, 6
features will be extracted with Statistical Analysis. After feature extraction, result of
multiple feature selection and extraction procedures will be combined by using
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FeatureUnion tool. At last, a Logistic Regression model will be created, and the pipeline
will be evaluated using 10-fold cross validation.
First, import the required packages as follows:
from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest
Now, we need to load the Pima diabetes dataset as did in previous examples:
path = r"C:\pima-indians-diabetes.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age',
'class']
data = read_csv(path, names=headernames)
array = data.values
Next, feature union will be created as follows:
features = []
features.append(('pca', PCA(n_components=3)))
features.append(('select_best', SelectKBest(k=6)))
feature_union = FeatureUnion(features)
Next, pipeline will be creating with the help of following script lines:
estimators = []
estimators.append(('feature_union', feature_union))
estimators.append(('logistic', LogisticRegression()))
model = Pipeline(estimators)
At last, we are going to evaluate this pipeline and output its accuracy as follows:
kfold = KFold(n_splits=20, random_state=7)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())
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Output
0.7789811066126855
The above output is the summary of accuracy of the setup on the dataset.