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我正在尝试使用Sktime对一组作为实验组织的 JSON 文件进行多变量分类。

输入是以下结构：

[
  { 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 }
]

分类标签设置为形状为 (len(files), 1) 的 DataFrame。以下是我的六个文件的实现。X 的结果形状是 (9528, 5) 并且应该是六行，每行包含文件的 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帧的信息，其中每一行表示编码或未编码的 JSON 或 CSV 或其他表示没有时间戳的时间序列的对象的列表。我看到了 JSON 被编码为数字键的例子，而其他的则有字符串。到目前为止，我找不到任何可以帮助我在一系列文件上使用这些列表构建数据框的东西。

4

1 回答 1

0

原来我正在寻找在 DataFrame 中嵌套 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)
于 2020-10-23T17:22:56.700 回答