Scikit Learn Learn InlyationForest：如何使用不同的参数拟合多个数据框（不使用GridSearchCV）

Question

我有一个五个单独的pandas数据框架，我将其放在字典中。 我想在Scikit-Learn中运行五个单独的隔离式模型 每个模型的参数集。但是，我不想隔离每个型号。

所以我的问题是，如何运行这些模型并获得决策功能， 对所有数据范围的所有数据范围的预测。我这样做的尝试在下面。

# parameters for each of the five models in a list. The index position in the list
# represents a dataset's parameters, from dataset0 through dataset4

n_estimators = [150, 200, 125, 125, 125]
max_samples = [0.70, 0.70, 0.80, 0.70, 0.70]
max_features = [1, 4, 2, 2, 3]
contamination = [0.05, 0.06, 0.05, 0.07, 0.05]

# numeric columns
num_columns = list(subset_features[1:])

# column transformer
num_transformer = Pipeline([('impute', IterativeImputer()), ('scale', StandardScaler())])
ct = ColumnTransformer([('num_pipeline', num_transformer, num_columns)])

clf = Pipeline([('ct', ct),
                ('iforest', IsolationForest(n_estimators=n_estimators[i],
                                            max_samples=max_samples[i],
                                            max_features=max_features[i],
                                            contamination=contamination[i],
                                            n_jobs=4,
                                            random_state=None))])



clf_res = {}
for i, df in enumerate(dfs.values()):
    print('starting idx:', i)
    clf_res[i] = clf.fit(df)

我遇到的问题是，它不是通过不同的参数集迭代 数据范围从迭代变为迭代。请参阅下文：

{0: Pipeline(steps=[('ct',
                  ColumnTransformer(transformers=[('numeric_pipeline',
                                                   Pipeline(steps=[('impute',
                                                                    IterativeImputer()),
                                                                   ('scale',
                                                                    StandardScaler())]),
                                                   ['V1', 'V2', 'V3',
                                                    'V4'])])),
                 ('iforest',
                  IsolationForest(contamination=0.05, max_features=1,
                                  max_samples=0.7, n_estimators=125,
                                  n_jobs=4))]),
 1: Pipeline(steps=[('ct',
                  ColumnTransformer(transformers=[('numeric_pipeline',
                                                   Pipeline(steps=[('impute',
                                                                    IterativeImputer()),
                                                                   ('scale',
                                                                    StandardScaler())]),
                                                   ['V1', 'V2', 'V3',
                                                    'V4'])])),
                 ('iforest',
                  IsolationForest(contamination=0.05, max_features=1,
                                  max_samples=0.7, n_estimators=125,
                                  n_jobs=4))])

因此，我想要的是，随着数据范围的变化，参数将会发生变化。

Answer 1

经过仔细审查我的代码以及一些谷歌搜索后，我发现了我的代码出了问题。我将在这里为未来可能有类似问题的其他任何人分享。

我对代码的唯一更改是将CLF Pipeline带入for循环。

# parameters for each of the five models in a list. The index position in the list
# represents a dataset's parameters, from dataset0 through dataset3

n_estimators = [150, 200, 125, 125, 125]
max_samples = [0.70, 0.70, 0.80, 0.70, 0.70]
max_features = [1, 4, 2, 2, 3]
contamination = [0.05, 0.06, 0.05, 0.07, 0.05]

# numeric columns
num_columns = list(subset_features[1:])

# column transformer
num_transformer = Pipeline([('impute', IterativeImputer()), ('scale', StandardScaler())])
ct = ColumnTransformer([('num_pipeline', num_transformer, num_columns)])

clf_res = {}
for i, df in enumerate(dfs.values()):
    print('starting idx:', i)
    clf = Pipeline([('ct', ct),
                    ('iforest', IsolationForest(n_estimators=n_estimators[i],
                                                max_samples=max_samples[i],
                                                max_features=max_features[i],
                                                contamination=contamination[i],
                                                n_jobs=4,
                                                random_state=None))])
    clf_res[i] = clf.fit(df)

正确输出的样本在下面

{0: Pipeline(steps=[('ct',
                  ColumnTransformer(transformers=[('numeric_pipeline',
                                                   Pipeline(steps=[('impute',
                                                                    IterativeImputer()),
                                                                   ('scale',
                                                                    StandardScaler())]),
                                                   ['v1', 'v2', 'v3',
                                                    'v4'])])),
                 ('iforest',
                  IsolationForest(contamination=0.05, max_features=1,
                                  max_samples=0.70,
                                  n_estimators=150, n_jobs=4))]),
 1: Pipeline(steps=[('ct',
                  ColumnTransformer(transformers=[('numeric_pipeline',
                                                   Pipeline(steps=[('impute',
                                                                    IterativeImputer()),
                                                                   ('scale',
                                                                    StandardScaler())]),
                                                   ['v1', 'v2', 'v3',
                                                    'v4'])])),
                 ('iforest',
                  IsolationForest(contamination=0.05, max_features=4,
                                  max_samples=0.7, n_estimators=200,
                                  n_jobs=4))]),