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Apply PCA to a CSV file and plot its datapoints (one per line).Usage: pca.py <csv_file>
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"""Apply PCA to a CSV file and plot its datapoints (one per line). | |
The first column should be a category (determines the color of each datapoint), | |
the second a label (shown alongside each datapoint).""" | |
import sys | |
import pandas | |
import pylab as pl | |
from sklearn import preprocessing | |
from sklearn.decomposition import PCA | |
def main(): | |
"""Load data.""" | |
try: | |
csvfile = sys.argv[1] | |
except IndexError: | |
print '%s\n\nUsage: %s [--3d] <csv_file>' % (__doc__, sys.argv[0]) | |
return | |
data = pandas.read_csv(csvfile, index_col=(0, 1)) | |
# first column provides labels | |
ylabels = [a for a, _ in data.index] | |
labels = [text for _, text in data.index] | |
encoder = preprocessing.LabelEncoder().fit(ylabels) | |
xdata = data.as_matrix(data.columns) | |
ydata = encoder.transform(ylabels) | |
target_names = encoder.classes_ | |
plotpca(xdata, ydata, target_names, labels, csvfile) | |
def plotpca(xdata, ydata, target_names, items, filename): | |
"""Make plot.""" | |
pca = PCA(n_components=2) | |
components = pca.fit(xdata).transform(xdata) | |
# Percentage of variance explained for each components | |
print('explained variance ratio (first two components):', | |
pca.explained_variance_ratio_) | |
pl.figure() # Make a plotting figure | |
pl.subplots_adjust(bottom=0.1) | |
# NB: a maximum of 7 targets will be plotted | |
for i, (c, m, target_name) in enumerate(zip( | |
'rbmkycg', 'o^s*v+x', target_names)): | |
pl.scatter(components[ydata == i, 0], components[ydata == i, 1], | |
color=c, marker=m, label=target_name) | |
for n, x, y in zip( | |
(ydata == i).nonzero()[0], | |
components[ydata == i, 0], | |
components[ydata == i, 1]): | |
pl.annotate( | |
items[n], | |
xy=(x, y), | |
xytext=(5, 5), | |
textcoords='offset points', | |
color=c, | |
fontsize='small', | |
ha='left', | |
va='top') | |
pl.legend() | |
pl.title('PCA of %s' % filename) | |
pl.show() | |
if __name__ == '__main__': | |
main() |
replace the line "xdata = data.as_matrix(data.columns)" with "xdata = data.values"
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AttributeError: 'DataFrame' object has no attribute 'as_matrix'
Getting this error when I tried to run for my own csv file. The code is as follows
import sys
import pandas
import pylab as pl
from sklearn import preprocessing
from tkinter.filedialog import asksaveasfilename, askopenfilename
from sklearn.decomposition import PCA
def main():
def plotpca(xdata, ydata, target_names, items, filename):
"""Make plot."""
pca = PCA(n_components=2)
components = pca.fit(xdata).transform(xdata)
if name == 'main':
main()