random_state=42, verbosity=2)
pipeline_optimizer.fit(X, y_int)
pipeline_optimizer.export('tpot_exported_pipeline.py')
if TEST_FEATURE_SELECTION:
fs.identify_missing(missing_threshold=0.6)
missing_features = fs.ops['missing']
missing_features[:10]
fs.plot_missing()
fs.missing_stats.head(10)
fs.identify_single_unique()
single_unique = fs.ops['single_unique']
single_unique
fs.plot_unique()
fs.identify_collinear(correlation_threshold=0.975)
correlated_features = fs.ops['collinear']
correlated_features[:5]
fs.plot_collinear()
#对于pandas，行标为index，列表为columns
#如常用df = pd.DataFrame(np.random.randn(5,3),index = list('abcde'),columns = ['one','two','three'])
#Create the Instance
fs = FeatureSelector(data=train, labels=train_labels)
# 1 Missing Values
fs.identify_missing(missing_threshold=0.6)
#The features identified for removal can be accessed through the ops dictionary of the FeatureSelector object.
missing_features = fs.ops['missing']
print(missing_features[:20])
fs.plot_missing() #在每一个画图的后面加上plt.show即可
plt.show()
print(fs.missing_stats.head(20))
# 2 Single Unique Value
fs.identify_single_unique()
single_unique = fs.ops['single_unique']
print(single_unique)
fs.plot_unique() #画图都不好用
plt.show()
print(fs.unique_stats.sample(5))
# 3 Collinear (highly correlated) Feature
