学习如何在外部数据集上使用局部线性嵌入

Question

使用以下网站： https://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-pyhttps://scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py

我设法在MNIST数据集和swissroll数据集上获得了LLE，但不知何故，我不知道如何使它在像https://www.kaggle.com/manufacturingai/predicting-fraud-w-fast-ai这样的外部数据集上运行

我的尝试如下：

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
from matplotlib import offsetbox
from sklearn import (manifold, datasets)

n_neighbors = 30
f_fontsize = 8
data = np.genfromtxt('../content/creditcard.csv', skip_header=True)
features = data[:, :3]
targets = data[:, 3]   # The last column is identified as the target

def plotcreditfraudfig(X, color, X_sr, err):

  fig = plt.figure()

  ax = fig.add_subplot(211, projection='3d')
  ax.scatter(X[:, 0], X[:, 1], X[:, 2],cmap=plt.cm.Spectral)

  ax.set_title("Original data")
  ax = fig.add_subplot(212)
  ax.scatter(X_sr[:, 0], X_sr[:, 1],cmap=plt.cm.Spectral)
  plt.axis('tight')
  plt.xticks([]), plt.yticks([])
  plt.title('Projected data')
  plt.show()

clf = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=2, method='standard')
clf.fit(X=features, y=targets)

print("Done. Reconstruction error: %g" %clf.reconstruction_error_)

X_llecf=clf.transform(X)
plot_embedding(X_llecf, "Locally Linear Embedding")

---------------------------------------------------------------------------

IndexError                                Traceback (most recent call last)

<ipython-input-106-91224a1ba194> in <module>()
      1 data = np.genfromtxt('../content/creditcard.csv', skip_header=True)
----> 2 features = data[:, :3]
      3 targets = data[:, 3]   # The last column is identified as the target
      4 
      5 clf = manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=2, method='standard')

IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed

Answer 1

我通过将功能和目标更改为：

X_features = data.drop('Class', axis=1)
y_targets = data['Class']

但我必须做更多： 因为矩阵不是半正定矩阵，所以在声明X_特征和y_目标之前，我必须清除一些行：

def clean_dataset(df):
    assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
    df.dropna(inplace=True)
    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
    return df[indices_to_keep].astype(np.float64)