def _get_svm_classifier(self, labels=None, test=None):
if labels is None:
return []
from ds4ml.utils import plot_confusion_matrix
svms = []
for col in labels:
in_test = (test is not None and col in test) or (test is None)
if in_test:
# When class label in svm classify test data, try to match
# two predicted result with the actual data, and so, there
# will be two confusion matrix diagrams.
src_matrix, tgt_matrix = self.classify(col, test=test)
vrange = (min(src_matrix.values.min(),
tgt_matrix.values.min()),
max(src_matrix.values.max(),
tgt_matrix.values.max()))
path = (plot_confusion_matrix(src_matrix,
vrange=vrange,
xlabel='raw',
ylabel='actual'),
plot_confusion_matrix(tgt_matrix,
vrange=vrange,
xlabel='synth',
ylabel='actual'))
svms.append({'column': col, 'path': path})
else:
# If not, will compare two predicted result.
matrix = self.classify(col, test=test)
# make path's type: 1-tuple
path = (plot_confusion_matrix(matrix,
xlabel='synth',
ylabel='raw'))
svms.append({'column': col, 'path': path})
return svms
def test_plot_figures_output_show():
from pandas import DataFrame
plot_confusion_matrix(DataFrame({
'True': [2, 3],
'False': [5, 0]
}),
otype='show')
plot_confusion_matrix(DataFrame({
'7th-8th': [2, 3, 5, 0],
'Masters': [0, 4, 1, 0],
'11th': [0, 1, 5, 2],
'Bachelors': [2, 0, 0, 6]
}),
otype='show')
bins = np.array([
28., 29.25, 30.5, 31.75, 33., 34.25, 35.5, 36.75, 38., 39.25, 40.5,
41.75, 43., 44.25, 45.5, 46.75, 48., 49.25, 50.5
])
counts = np.array(
[[1, 0, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[1, 0, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1]])
plot_histogram(bins, counts, otype='show')
bins = np.array(['Private', 'Self-emp-not-inc', 'State-gov'])
counts = np.array([[6, 2, 1], [6, 2, 1]])
plot_histogram(bins, counts, otype='show')
bins = np.array(['11th', '9th', 'Bachelors', 'HS-grad', 'Masters'])
counts = np.array([[3, 2, 2, 1, 1], [3, 2, 2, 1, 1]])
plot_histogram(bins, counts, otype='show')
bins = np.array([
5., 5.45, 5.9, 6.35, 6.8, 7.25, 7.7, 8.15, 8.6, 9.05, 9.5, 9.95, 10.4,
10.85, 11.3, 11.75, 12.2, 12.65, 13.1
])
counts = np.array(
[[1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],
[1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]])
plot_histogram(bins, counts, otype='show')
bins = np.array(['Female', 'Male'])
counts = np.array([[5, 4], [5, 4]])
plot_histogram(bins, counts, otype='show')
from .testdata import adults01
from ds4ml.metrics import pairwise_mutual_information
data = pairwise_mutual_information(DataFrame(adults01))
plot_heatmap(data, otype='show')
def test_plot_confusion_matrix_output_string():
from pandas import DataFrame
df = DataFrame({'True': [2, 3], 'False': [5, 0]})
res = plot_confusion_matrix(df)
assert type(res) == str
assert res.startswith('<svg')
assert res.endswith('</svg>')
def to_html(self,
buf=None,
title='Evaluation Report',
info=True,
distribute=True,
correlate=True,
classifier=None,
labels=None,
test=None):
"""
Render the evaluation result of two data set as an HTML file.
Parameters
----------
buf : optional
buffer to write to
title : str
title of evaluation report
info : bool, default true
show basic information of two data set, including relative error,
and Jensen-Shannon divergence (jsd).
distribute : bool, default true
show distribution of each attribute.
correlate : bool, default true
show correlation of pair-wise attributes.
classifier : str
use classifier to train data set on one or more columns (defined by
parameter 'label') and show prediction result on the evaluation
report. Optional classifier: SVM.
labels : list of column names
column name, or a list of column names separated by comma, used for
classification task.
test : pd.DataFrame
test data for classification, and other machine learning tasks.
"""
from ds4ml.utils import (plot_histogram, plot_heatmap,
plot_confusion_matrix)
from mako.template import Template
import os
old_cwd = os.getcwd()
os.chdir(os.path.dirname(__file__))
template = Template(filename='template/report.html')
os.chdir(old_cwd)
topics = []
content = {}
# format different kinds of evaluation result to unified style
if info:
topics.append('basic')
content['basic'] = [self.describe().to_dict('split')]
if distribute:
topics.append('dist')
content['dist'] = []
for col in self.columns:
bins, counts = self.dist(col)
svg = plot_histogram(bins, counts)
content['dist'].append({
'name': col,
'columns': bins,
'data': counts,
'path': svg
})
if correlate:
topics.append('corr')
content['corr'] = []
source_mi, target_mi = self.corr()
source_svg = plot_heatmap(source_mi)
target_svg = plot_heatmap(target_mi)
content['corr'].append({
'matrix': source_mi.to_dict('split'),
'path': source_svg
})
content['corr'].append({
'matrix': target_mi.to_dict('split'),
'path': target_svg
})
if labels is not None:
topics.append('svm')
content['svm'] = []
for col in labels:
in_test = (test is not None and col in test) or (test is None)
if in_test:
# When class label in svm classify test data, try to match
# two predicted result with the actual data, and so, there
# will be two confusion matrix diagrams.
try:
source_cm, target_cm = self.classify(col, test=test)
vrange = (min(source_cm.values.min(),
target_cm.values.min()),
max(source_cm.values.max(),
target_cm.values.max()))
path = (plot_confusion_matrix(source_cm,
vrange=vrange,
xlabel='raw',
ylabel='actual'),
plot_confusion_matrix(target_cm,
vrange=vrange,
xlabel='synth',
ylabel='actual'))
content['svm'].append({'column': col, 'path': path})
except ValueError as e:
print(e)
else:
# If not, will compare two predicted result.
try:
cm = self.classify(col, test=test)
# make path's type: 1-tuple
path = (plot_confusion_matrix(cm,
xlabel='synth',
ylabel='raw'), )
content['svm'].append({'column': col, 'path': path})
except ValueError as e:
print(e)
svms = content['svm'] if 'svm' in content else []
if buf:
with open(buf, 'w+', encoding='utf-8') as file:
file.write(
template.render(title=title,
basics=content['basic'],
dists=content['dist'],
corrs=content['corr'],
svms=svms))