Python 修改pca图，以减少每个图的尺寸
我有下面的代码和样本数据，这些数据绘制了pca的特征权重，以便于查看每个维度是由什么组成的。我的问题是，它试图在一个绘图中拟合所有维度。我只想在每个图中显示五个维度，或者在一行中显示五个维度，然后在下面显示五个维度，直到我的维度用完为止，这样更容易阅读。有谁能建议一种巧妙的方法来修改代码，使其在每个绘图和循环中显示5个维度，直到我超出维度 代码： 功能：Python 修改pca图，以减少每个图的尺寸,python,matplotlib,Python,Matplotlib,我有下面的代码和样本数据，这些数据绘制了pca的特征权重，以便于查看每个维度是由什么组成的。我的问题是，它试图在一个绘图中拟合所有维度。我只想在每个图中显示五个维度，或者在一行中显示五个维度，然后在下面显示五个维度，直到我的维度用完为止，这样更容易阅读。有谁能建议一种巧妙的方法来修改代码，使其在每个绘图和循环中显示5个维度，直到我超出维度 代码： 功能： ########################################### # Suppress matplotlib user
###########################################
# Suppress matplotlib user warnings
# Necessary for newer version of matplotlib
import warnings
warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
#
# Display inline matplotlib plots with IPython
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'inline')
###########################################
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pandas as pd
import numpy as np
def pca_results(good_data, pca):
'''
Create a DataFrame of the PCA results
Includes dimension feature weights and explained variance
Visualizes the PCA results
'''
# Dimension indexing
dimensions = dimensions = ['Dimension {}'.format(i) for i in range(1,len(pca.components_)+1)]
# PCA components
components = pd.DataFrame(np.round(pca.components_, 4), columns = good_data.keys())
components.index = dimensions
# PCA explained variance
ratios = pca.explained_variance_ratio_.reshape(len(pca.components_), 1)
variance_ratios = pd.DataFrame(np.round(ratios, 4), columns = ['Explained Variance'])
variance_ratios.index = dimensions
# Create a bar plot visualization
fig, ax = plt.subplots(figsize = (14,8))
# Plot the feature weights as a function of the components
components.plot(ax = ax, kind = 'bar');
ax.set_ylabel("Feature Weights")
ax.set_xticklabels(dimensions, rotation=0)
# Display the explained variance ratios
for i, ev in enumerate(pca.explained_variance_ratio_):
ax.text(i-0.40, ax.get_ylim()[1] + 0.05, "Explained Variance\n %.4f"%(ev))
# Return a concatenated DataFrame
return pd.concat([variance_ratios, components], axis = 1)
尝试用以下代码替换打印代码：
# Configure the number of dims to show per subplot
dims_per_plot = dpp = 5
# Prepare plot with appropriate number of subplots
# Note: see [1]
plot_rows = -(-len(dimensions) // dims_per_plot)
fig, axes = plt.subplots(plot_rows, 1, figsize = (14,8*plot_rows))
# For each subplot...
for c, ax in enumerate(axes):
# Plot the appropriate components
components.iloc[c*dpp:c*dpp+dpp].plot(ax=ax, kind='bar');
ax.set_ylabel("Feature Weights")
# Configure the xticks
# Note: set_xticks is necessary for correct display of partially filled plots
ax.set_xticks(range(dpp+1))
ax.set_xticklabels(dimensions[c*dpp:c*dpp+dpp], rotation=0)
# Display the explained variance ratios
# Note: the ha and multialignment kwargs allow centering of (multiline) text
for i, ev in enumerate(pca.explained_variance_ratio_[c*dpp:c*dpp+dpp]):
ax.text(i, ax.get_ylim()[1] + 0.02,
"Explained Variance\n%.4f" % (ev),
ha='center', multialignment='center')
# Done
plt.show()
print log_data.iloc[1:50]
OrderCnt Damaged_Spoiled Item_Inc missing Other P_rep \
1 0.693147 0.000000 0.000000 0.000000 0.000000 0.000000
5 1.098612 0.000000 0.000000 0.000000 0.000000 0.000000
6 1.791759 0.000000 0.000000 0.000000 0.000000 0.000000
7 2.564949 0.000000 0.000000 0.000000 0.000000 0.000000
8 2.833213 0.000000 0.000000 0.000000 0.000000 0.000000
9 3.433987 1.098612 0.000000 0.693147 0.000000 0.000000
10 3.332205 1.098612 0.000000 0.000000 0.000000 0.000000
11 3.091042 0.000000 0.000000 0.000000 0.000000 0.000000
12 3.178054 0.000000 0.000000 0.000000 0.000000 0.000000
13 3.332205 0.000000 0.000000 0.000000 0.000000 0.000000
14 3.637586 0.000000 0.693147 0.693147 0.000000 0.000000
15 3.610918 0.000000 0.000000 0.000000 0.000000 0.000000
16 3.583519 0.000000 0.000000 0.000000 0.000000 0.000000
17 3.433987 0.000000 0.000000 0.000000 0.000000 0.000000
18 3.332205 0.000000 0.693147 0.000000 0.000000 0.000000
19 2.890372 0.000000 0.000000 0.000000 0.000000 0.000000
20 2.197225 0.000000 0.000000 0.000000 0.000000 0.000000
21 1.098612 0.000000 0.000000 0.000000 0.000000 0.000000
22 0.693147 0.000000 0.000000 0.000000 0.000000 0.000000
28 0.693147 0.000000 0.000000 0.000000 0.000000 0.000000
29 1.098612 0.000000 0.000000 0.000000 0.000000 0.000000
30 1.945910 0.000000 0.000000 0.000000 0.000000 0.000000
31 2.302585 0.000000 0.000000 0.000000 0.000000 0.000000
32 2.995732 0.000000 0.000000 1.098612 0.000000 0.000000
33 3.367296 0.693147 0.000000 0.693147 0.000000 0.000000
34 3.332205 0.693147 0.000000 0.693147 0.000000 0.000000
35 3.367296 0.693147 0.000000 0.000000 0.000000 0.000000
36 3.135494 0.000000 0.000000 0.693147 0.000000 0.000000
37 3.401197 0.000000 0.000000 0.000000 0.000000 0.000000
38 3.737670 0.000000 0.000000 0.693147 0.000000 0.000000
39 3.583519 0.693147 0.000000 0.000000 0.000000 0.000000
40 3.784190 0.693147 0.000000 0.000000 0.000000 0.000000
41 3.761200 0.693147 0.000000 1.098612 0.000000 0.000000
42 3.332205 0.000000 0.000000 0.000000 0.000000 0.693147
43 2.833213 0.000000 0.000000 0.000000 0.000000 0.000000
44 2.397895 1.098612 0.000000 0.000000 0.000000 0.000000
45 1.098612 0.000000 0.000000 0.000000 0.000000 0.000000
46 0.693147 0.000000 0.000000 0.000000 0.000000 0.000000
53 2.079442 0.000000 0.000000 0.000000 0.000000 0.000000
54 2.079442 0.000000 0.000000 0.000000 0.000000 0.000000
55 2.944439 0.000000 0.000000 0.000000 0.000000 0.000000
56 3.465736 0.693147 0.000000 0.000000 0.000000 0.000000
57 3.828641 1.386294 0.000000 1.098612 0.000000 0.693147
58 3.688879 0.000000 0.000000 0.693147 0.000000 0.000000
59 3.737670 0.000000 0.000000 1.386294 0.693147 0.693147
60 3.737670 0.693147 0.000000 0.000000 0.000000 0.000000
61 3.761200 0.000000 0.000000 0.000000 0.000000 0.000000
62 3.761200 0.000000 0.000000 0.693147 0.000000 0.000000
63 3.583519 0.000000 0.000000 0.000000 0.000000 0.000000
P_serv Wrong_item chi nyc sf rate_0 rate_1 \
1 0.693147 0.000000 0.000000 0.000000 0.693147 0.000000 0.000000
5 0.000000 0.000000 1.098612 0.000000 0.000000 0.000000 0.000000
6 0.000000 0.000000 1.791759 0.000000 0.000000 0.000000 0.000000
7 0.693147 1.386294 2.079442 0.693147 1.791759 0.000000 0.000000
8 0.000000 0.000000 2.079442 1.386294 1.945910 0.000000 0.000000
9 1.098612 1.386294 2.564949 0.693147 2.995732 0.000000 1.386294
10 1.098612 0.000000 2.397895 1.098612 2.890372 0.000000 1.386294
11 0.000000 0.000000 2.397895 1.791759 1.945910 0.000000 0.693147
12 0.000000 0.693147 2.079442 1.609438 2.564949 0.000000 0.693147
13 0.000000 0.000000 2.708050 1.098612 2.564949 0.000000 0.693147
14 0.000000 0.693147 2.772589 1.945910 2.833213 0.000000 0.000000
15 0.000000 0.000000 2.708050 2.484907 2.564949 0.693147 0.000000
16 0.000000 0.693147 2.833213 1.791759 2.708050 0.693147 0.000000
17 0.000000 0.693147 2.833213 1.791759 2.397895 0.000000 0.000000
18 0.000000 1.098612 2.197225 1.098612 2.890372 0.000000 0.000000
19 0.000000 0.000000 1.945910 1.098612 2.397895 0.000000 0.000000
20 0.000000 0.000000 0.000000 0.000000 2.197225 0.000000 0.000000
21 0.000000 0.000000 0.000000 0.000000 1.098612 0.000000 0.000000
22 0.000000 0.000000 0.000000 0.000000 0.693147 0.000000 0.000000
28 0.000000 0.000000 0.000000 0.693147 0.000000 0.000000 0.000000
29 0.000000 0.000000 0.693147 0.693147 0.000000 0.000000 0.000000
30 0.000000 0.000000 1.791759 0.000000 0.693147 0.000000 0.000000
31 0.000000 0.000000 2.197225 1.386294 0.000000 0.000000 0.000000
32 0.000000 0.000000 2.079442 1.098612 2.484907 0.000000 0.000000
33 0.000000 0.693147 2.397895 0.693147 2.890372 0.000000 0.000000
34 0.000000 1.098612 2.708050 1.098612 2.772589 0.000000 0.693147
35 0.000000 1.386294 2.564949 1.098612 2.772589 0.000000 0.000000
36 0.000000 0.000000 2.397895 1.609438 2.197225 0.000000 0.693147
37 0.000000 1.098612 2.639057 0.693147 2.890372 0.000000 0.693147
38 0.000000 0.000000 3.135494 1.386294 2.833213 0.000000 0.693147
39 0.000000 0.000000 2.890372 1.791759 2.639057 0.000000 0.000000
40 0.000000 1.098612 3.295837 1.386294 2.772589 1.386294 0.000000
41 0.000000 1.098612 2.944439 1.098612 3.218876 0.000000 1.098612
42 0.693147 0.693147 2.302585 1.098612 2.944439 0.000000 0.000000
43 0.000000 0.000000 1.098612 0.000000 2.772589 0.000000 0.000000
44 1.098612 0.000000 0.000000 0.000000 2.397895 0.000000 0.000000
45 0.000000 0.000000 0.000000 0.000000 1.098612 0.000000 0.000000
46 0.000000 0.000000 0.000000 0.000000 0.693147 0.000000 0.000000
53 0.000000 0.000000 1.609438 1.098612 0.693147 0.000000 0.000000
54 0.000000 0.000000 1.945910 0.693147 0.693147 0.000000 0.000000
55 0.000000 0.000000 2.564949 1.791759 0.693147 0.693147 0.000000
56 0.000000 0.000000 2.564949 2.079442 2.564949 0.000000 0.000000
57 0.693147 0.693147 2.995732 2.079442 3.258097 0.693147 0.000000
58 0.000000 0.000000 3.218876 1.386294 2.708050 0.000000 0.000000
59 0.000000 0.693147 3.135494 1.386294 2.995732 0.000000 0.693147
60 0.000000 1.098612 2.833213 1.098612 3.178054 0.000000 0.000000
61 0.000000 1.098612 3.044522 1.791759 3.044522 0.000000 0.000000
62 0.693147 0.000000 3.044522 1.098612 3.135494 0.000000 0.000000
63 0.693147 0.000000 2.833213 1.098612 2.890372 0.000000 0.000000
rate_2 rate_3 rate_4 rate_5
1 0.000000 0.693147 0.000000 0.000000
5 0.000000 0.000000 0.000000 1.098612
6 0.000000 0.000000 0.000000 1.791759
7 1.098612 0.000000 1.098612 2.302585
8 0.693147 0.693147 1.098612 2.564949
9 1.386294 0.693147 1.609438 3.091042
10 0.000000 0.693147 1.386294 3.135494
11 0.000000 0.693147 1.098612 2.890372
12 0.000000 0.000000 1.609438 2.944439
13 0.693147 0.000000 0.693147 3.258097
14 0.000000 1.098612 1.098612 3.526361
15 0.693147 0.693147 1.386294 3.465736
16 0.693147 1.386294 0.693147 3.401197
17 0.000000 0.693147 1.791759 3.258097
18 0.693147 1.098612 1.386294 3.091042
19 0.693147 0.000000 0.693147 2.833213
20 0.000000 0.000000 0.693147 2.079442
21 0.000000 0.000000 0.000000 1.098612
22 0.000000 0.000000 0.693147 0.000000
28 0.000000 0.000000 0.693147 0.000000
29 0.000000 0.000000 0.000000 1.098612
30 0.000000 0.000000 0.693147 1.791759
31 0.000000 0.000000 0.000000 2.484907
32 0.693147 0.693147 1.609438 2.708050
33 0.000000 1.098612 2.079442 2.995732
34 0.000000 0.000000 2.079442 3.178054
35 0.693147 0.693147 1.945910 3.091042
36 0.000000 0.693147 0.000000 3.044522
37 1.098612 0.000000 1.386294 3.258097
38 0.693147 1.098612 1.098612 3.583519
39 0.000000 0.000000 1.791759 3.433987
40 0.000000 1.098612 2.079442 3.496508
41 0.693147 1.098612 2.079442 3.496508
42 0.693147 0.000000 0.693147 3.332205
43 0.000000 0.693147 1.098612 2.708050
44 0.693147 0.000000 1.609438 1.791759
45 0.000000 0.000000 0.693147 0.693147
46 0.000000 0.000000 0.000000 0.693147
53 0.000000 0.693147 0.693147 1.791759
54 0.000000 0.000000 1.098612 1.945910
55 0.000000 0.693147 1.098612 2.708050
56 0.000000 1.098612 2.079442 3.135494
57 0.000000 1.791759 2.197225 3.637586
58 0.000000 1.098612 1.791759 3.555348
59 0.693147 1.945910 1.791759 3.465736
60 0.000000 0.693147 1.945910 3.555348
61 0.693147 1.098612 1.609438 3.663562
62 0.000000 1.386294 1.386294 3.663562
63 0.693147 0.000000 1.609438 3.433987
# Configure the number of dims to show per subplot
dims_per_plot = dpp = 5
# Prepare plot with appropriate number of subplots
# Note: see [1]
plot_rows = -(-len(dimensions) // dims_per_plot)
fig, axes = plt.subplots(plot_rows, 1, figsize = (14,8*plot_rows))
# For each subplot...
for c, ax in enumerate(axes):
# Plot the appropriate components
components.iloc[c*dpp:c*dpp+dpp].plot(ax=ax, kind='bar');
ax.set_ylabel("Feature Weights")
# Configure the xticks
# Note: set_xticks is necessary for correct display of partially filled plots
ax.set_xticks(range(dpp+1))
ax.set_xticklabels(dimensions[c*dpp:c*dpp+dpp], rotation=0)
# Display the explained variance ratios
# Note: the ha and multialignment kwargs allow centering of (multiline) text
for i, ev in enumerate(pca.explained_variance_ratio_[c*dpp:c*dpp+dpp]):
ax.text(i, ax.get_ylim()[1] + 0.02,
"Explained Variance\n%.4f" % (ev),
ha='center', multialignment='center')
# Done
plt.show()