ARIMA 時間序列3: 使用tsfresh庫進行分類任務

tsfresh是開源的提取時序數據特徵的python包，能夠提取出超過4000種特徵 .

1. 查看數據

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from tsfresh.example.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures
from tsfresh import extract_features, extract_relevant_features, select_features
from tsfresh.utilities.dataframe_functions import impute
from tsfresh.feature_extraction import ComprehensiveFCParameters
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

download_robot_execution_failures()
df, y = load_robot_execution_failures()
df.head()

df[df.id == 2][['time','F_x','F_y','F_z','T_x','T_y',
                'T_z']].plot(x='time', title='Success example (id 2)', figsize = (12, 6))
df[df.id == 25][['time','F_x','F_y','F_z','T_x','T_y',
                 'T_z']].plot(x='time', title='Failure example (id 25)', figsize = (12, 6))

2. 時間序列特徵提取

extraction_settings = ComprehensiveFCParameters()
X = extraction_features(df, column_id='id', column_sort='time',
                    default_fc_parameters=extraction_settings, impute_function=impute)

print(X.shape)
X.head()

X.info()

• 特徵過濾

X_filtered = extract_relevant_features(df, y, column_id='id', column_sort='time',
                                     default_fc_parameters=extraction_settings)

print(X_filtered.shape)
X_filtered.head()

X_filtered.info()

3. 用決策樹訓練,預測和評估模型

X_train,X_test,X_filtered_train,X_filtered_test,y_train,y_test = train_test_split(X,X_filtered,y,test_size=0.3)

dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
print(classification_report(y_test, dtc.predict(X_test)))
dtc.n_features_

4524

dtc2 = DecisionTreeClassifier()
dtc2.fit(X_filtered_train, y_train)
print(classification_report(y_test, dtc2.predict(X_filtered_test)))
dtc2.n_features_

621