I am getting a ValueError: shape mismatch: objects cannot be broadcast to single shape.
The error occurs when I run the following code:
plt.bar(range(1, 14), pca.explained_variance_ratio_, alpha=0.5,
... align='center')
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/me/anaconda3/envs/new36/lib/python3.6/site-packages/matplotlib/pyplot.py", line 2648, in bar
ret = ax.bar(*args, **kwargs)
File "/me/anaconda3/envs/new36/lib/python3.6/site-packages/matplotlib/__init__.py", line 1717, in inner
return func(ax, *args, **kwargs)
File "/me/anaconda3/envs/new36/lib/python3.6/site-packages/matplotlib/axes/_axes.py", line 2019, in bar
np.atleast_1d(x), height, width, y, linewidth)
File "/me/anaconda3/envs/new36/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 249, in broadcast_arrays
shape = _broadcast_shape(*args)
File "/me/anaconda3/envs/new36/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 184, in _broadcast_shape
b = np.broadcast(*args[:32])
ValueError: shape mismatch: objects cannot be broadcast to a single shape
This is the full code used to replicate the error:
from sklearn.datasets.samples_generator import make_blobs
from pandas import DataFrame
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
# generate a classification dataset
X, y = make_blobs(n_samples=1000, centers=3, n_features=10, random_state=1,
cluster_std=3)
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.2,
stratify=y,
random_state=0)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.transform(X_test)
pca = PCA()
X_train_pca = pca.fit_transform(X_train_std)
pca.explained_variance_ratio_
import matplotlib.pyplot as plt
import numpy as np
# Error occurs here
plt.bar(range(1, 14), pca.explained_variance_ratio_, alpha=0.5,
align='center')
plt.step(range(1, 14), np.cumsum(pca.explained_variance_ratio_), where='mid')
plt.ylabel('Explained variance ratio')
plt.xlabel('Principal components')
plt.show()
It's because the size of your range is not the same size as your pca variance ratio. The same error can be replicated with two separate ranges:
# same ranges are ok
plt.bar(range(10), range(10))
<Container object of 10 artists>
# different ones are not
plt.bar(range(15), range(10))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/mm92400/anaconda3/envs/new36/lib/python3.6/site-packages/matplotlib/pyplot.py", line 2648, in bar
ret = ax.bar(*args, **kwargs)
File "/Users/mm92400/anaconda3/envs/new36/lib/python3.6/site-packages/matplotlib/__init__.py", line 1717, in inner
return func(ax, *args, **kwargs)
File "/Users/mm92400/anaconda3/envs/new36/lib/python3.6/site-packages/matplotlib/axes/_axes.py", line 2019, in bar
np.atleast_1d(x), height, width, y, linewidth)
File "/Users/mm92400/anaconda3/envs/new36/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 249, in broadcast_arrays
shape = _broadcast_shape(*args)
File "/Users/mm92400/anaconda3/envs/new36/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 184, in _broadcast_shape
b = np.broadcast(*args[:32])
ValueError: shape mismatch: objects cannot be broadcast to a single shape
So use a range with the same length as your pca args:
x = pca.explained_variance_ratio_.shape[0]
plt.bar(
range(1, x+1),
pca.explained_variance_ratio_,
alpha=0.5,
align='center'
)