Which algorithm to use to classify plots based on graphical features?

Question

I have plots which I need to classify based on some features. For example, I need to differentiate between the following plots having smooth features or 'valleys' at certain x values. Which machine learning algorithm would be most appropriate to do so? I was thinking a combination of anomaly detection, clustering and classification. Any help is appreciated. Thank you!

Answer 1

Your question is unclear. Please provide more information about the dataset, the dataset size, the target, the motivation. Until then I can only assume and try to help nontheless.

Assumptions

Since you speak of classification I assume that you have labels. I further assume that you have access to the underlying data used to produce the plot. In that exact case, the following will be useful:

Solution

Instead of classifying the plots, you could classify the underlying two-dimensional datasets treating them as time-series.

For your specific case, Dynamic Time Warping distance could be useful. You could therefore use this code:

import numpy as np
from scipy.spatial import distance
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report

#toy dataset 
X = np.random.random((100,10))
y = np.random.randint(0,2, (100))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

#custom metric
def DTW(a, b):   
    an = a.size
    bn = b.size
    pointwise_distance = distance.cdist(a.reshape(-1,1),b.reshape(-1,1))
    cumdist = np.matrix(np.ones((an+1,bn+1)) * np.inf)
    cumdist[0,0] = 0

    for ai in range(an):
        for bi in range(bn):
            minimum_cost = np.min([cumdist[ai, bi+1],
                                   cumdist[ai+1, bi],
                                   cumdist[ai, bi]])
            cumdist[ai+1, bi+1] = pointwise_distance[ai,bi] + minimum_cost

    return cumdist[an, bn]

#train
parameters = {'n_neighbors':[2, 4, 8]}
clf = GridSearchCV(KNeighborsClassifier(metric =DTW), parameters, cv=5)
clf.fit(X_train, y_train)

#evaluate
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))