JSON列表中的Dataframe行，用于使用scikit训练ML

Question

我试图用斯考尔对一组作为实验组织的JSON文件进行多元分类。

输入的结构如下：

[
  { v: 431, t: 2, d1: 986000, d2: 434000, X: 0 },
  { v: 77, t: 0, d1: 47000, d2: 613000, X: 0 },
  { v: 58, t: 1, d1: 197000, d2: 47000, X: 0 },
  { v: 77, t: 0, d1: 260000, d2: 213000, X: 0 }
]

用于分类的标签被设置为具有形状的DataFrame (len(文件)，1)。下面是我的六个文件的实现。X的结果形状是(9528，5)，应该是六行，每一行都包含文件的JSON：

import json
import pandas as pd
import numpy as np
from pandas import json_normalize
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sktime.classification.compose import ColumnEnsembleClassifier
from sktime.classification.compose import TimeSeriesForestClassifier
from sktime.classification.dictionary_based import BOSSEnsemble
# from sktime.classification.shapelet_based import MrSEQLClassifier
from sktime.datasets import load_basic_motions
from sktime.transformers.series_as_features.compose import ColumnConcatenator
from sklearn.model_selection import train_test_split


controls = [
    '_clean_control01.json',
    '_clean_control02.json',
    '_clean_control03.json',
]

exp = [
    '_clean_exp01.json',
    '_clean_exp02.json',
    '_clean_exp03.json',
]

testsets = {
    'control': controls,
    'exp': exp
}

map_experiments = {
    'control': 0,
    'exp': 1
}

normalized_data = {
    'control': [],
    'exp': []
}

experiments = pd.DataFrame()
labels = {'exp': []}

for experiment in testsets:
    files = testsets[experiment]
    arr = normalized_data[experiment]
    for file in files:
        tmp = pd.read_json(file)
        experiments = experiments.append(tmp, ignore_index=True)
        label = map_experiments[experiment]
        labels['exp'].append(label)

labels = pd.DataFrame(labels)

X, y = experiments, labels
X_train, X_test, y_train, y_test = train_test_split(
    X, y, shuffle=False, stratify=None)
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
print(X_train.head())
np.unique(y_train)

clf = ColumnEnsembleClassifier(estimators=[
    ("TSF0", TimeSeriesForestClassifier(n_estimators=100), [0]),
    ("BOSSEnsemble3", BOSSEnsemble(max_ensemble_size=5), [3]),
])
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))

我很难找到如何构建数据格式的信息，其中每一行表示编码或未编码的JSON或CSV的列表，或者表示没有时间戳的时间序列的其他对象。我看到了一些示例，其中JSON被编码为数字键，而其他示例则有字符串。到目前为止，我找不到任何东西可以帮助我在一系列文件上使用这些列表构建数据框架。

Answer 1

原来，我在DataFrame中寻找嵌套的ndarray，如下所示：

    experiments = pd.DataFrame(['exp'])
    for file in files:
        tmp = pd.read_json(file).to_numpy()
        experiments = experiments.append({'exp': tmp}, ignore_index=True)