def two_years_analysis(two_years_df
, first_metric
, second_metric
, key):
print()
print("Co-change"
, first_metric
, second_metric)
g = two_years_df.groupby([first_metric, second_metric]
, as_index=False).agg({key : 'count'})
print(g)
cm = ConfusionMatrix(g_df=g
, classifier=first_metric
, concept=second_metric, count=key)
print(cm.summarize())
print()
print("Samples", cm.samples())
print("Both metrics increment match", cm.accuracy())
print(second_metric
, " improvement given "
, first_metric
, " improvement", cm.precision(), "lift", cm.precision_lift())
print(first_metric
, " improvement given "
, second_metric
, "improvement", cm.recall(), "lift", ifnull(safe_divide(ifnull(cm.recall()),cm.hit_rate())) - 1)
print()
from confusion_matrix import ConfusionMatrix
import cv2
import numpy as np
matrix = ConfusionMatrix(size=20)
names = open('/media/rgh/rgh-data/Dataset/Lip_320/val.txt', 'r')
gt_label_dir = '/media/rgh/rgh-data/Dataset/Lip_320/label/val/'
predict_dir = '/'
for index, name in enumerate(names):
name = name.strip()
print index, name
gt = cv2.imread(gt_label_dir + name + '.png', flags=cv2.IMREAD_GRAYSCALE)
result = cv2.imread(predict_dir + name + '.png',
flags=cv2.IMREAD_GRAYSCALE)
matrix.update(gt, result)
print matrix.accuracy(), matrix.fg_accuracy(), matrix.avg_precision(
), matrix.avg_recall(), matrix.avg_f1score()
print matrix.f1score()
def quality_and_speed_over_years(commits_per_user_file):
print("over the years ccp and speed change")
trep = get_valid_repos()
trep = trep[['repo_name']]
users_per_project = pd.read_csv(commits_per_user_file)
users_per_project = users_per_project[users_per_project.year > 2014]
df = pd.merge(users_per_project, trep, on='repo_name')
df = df[[
'repo_name', 'year', 'corrective_commits_ratio',
'commits_per_above11_users'
]]
df = df.dropna()
cur_df = df.copy()
cur_df['prev_year'] = cur_df.year - 1
cur_df = cur_df.rename(
columns={
'year': 'cur_year',
'corrective_commits_ratio': 'cur_corrective_commits_ratio',
'commits_per_above11_users': 'cur_commits_per_above11_users'
})
prev_df = df.copy()
prev_df = prev_df.rename(
columns={
'year': 'prev_year',
'corrective_commits_ratio': 'prev_corrective_commits_ratio',
'commits_per_above11_users': 'prev_commits_per_above11_users'
})
two_years = pd.merge(cur_df,
prev_df,
left_on=['repo_name', 'prev_year'],
right_on=['repo_name', 'prev_year'])
two_years[
'improved_ccp'] = two_years.cur_corrective_commits_ratio < two_years.prev_corrective_commits_ratio
two_years[
'hurt_ccp'] = two_years.cur_corrective_commits_ratio > two_years.prev_corrective_commits_ratio
two_years[
'improved_speed'] = two_years.cur_commits_per_above11_users > two_years.prev_commits_per_above11_users
g = two_years.groupby(['improved_ccp', 'improved_speed'],
as_index=False).agg({'repo_name': 'count'})
print(g)
cm = ConfusionMatrix(g_df=g,
classifier='improved_ccp',
concept='improved_speed',
count='repo_name')
print(cm.summarize())
print("speed & ccp improvement match", cm.accuracy())
print("speed improvement given ccp improvement", cm.precision())
print("ccp improvement given speed improvement",
cm.tp() / (cm.fn() + cm.tp()))
two_years[
'sig_improved_ccp'] = two_years.cur_corrective_commits_ratio < two_years.prev_corrective_commits_ratio - 0.1
two_years[
'sig_improved_speed'] = two_years.cur_commits_per_above11_users > two_years.prev_commits_per_above11_users + 10
g = two_years.groupby(['sig_improved_ccp', 'sig_improved_speed'],
as_index=False).agg({'repo_name': 'count'})
print(g)
cm = ConfusionMatrix(g_df=g,
classifier='sig_improved_ccp',
concept='sig_improved_speed',
count='repo_name')
print(cm.summarize())
g = two_years.groupby(['sig_improved_ccp', 'improved_speed'],
as_index=False).agg({'repo_name': 'count'})
print(g)
print(cm.summarize())
print()
print("speed & ccp improvement match", cm.accuracy())
print("speed improvement given ccp improvement", cm.precision(), "lift",
cm.precision_lift())
print("ccp improvement given speed improvement", cm.recall(), "lift",
cm.recall() / cm.hit_rate() - 1)
print()
g = two_years.groupby(['sig_improved_speed', 'hurt_ccp'],
as_index=False).agg({'repo_name': 'count'})
cm = ConfusionMatrix(g_df=g,
classifier='sig_improved_speed',
concept='hurt_ccp',
count='repo_name')
print(cm.summarize())
print()
print("ccp hurt given significant speed improvement", cm.precision(),
"lift", cm.precision_lift())
print()