In [258]:
%reset -f
In [282]:
from IPython.display import display, HTML
from sklearn import metrics
from sklearn import cross_validation
from scipy import interp
import pandas as pd
import numpy as np
import os
import sys
sys.path.insert(1, "/home/aegle/projects/myosa/src/")
from TypeFeatImputer import TypeFeatImputer
from i_score_parallel import i_score
from TypeFeatFS import DiscreteFS, ContinuousFS
from TypeFeatFilter import DiscreteFilter, ContinuousFilter
from OutlierFiltering import OutlierFiltering
from typeFeat_score import typeFeat_score
from pylab import *
import seaborn as sns
import matplotlib.pyplot as plt
np.set_printoptions(suppress=True)
pd.options.display.float_format = '{:,.2f}'.format
plt.style.use('classic')
%matplotlib inline
In [298]:
folder = "equal_more_four" #"more_than_four"
In [302]:
dfAll =[]
datasets = [0,1,2]
for ds in datasets:
pathDir = os.path.join('resources','results',folder,'ds_' + str(ds))
path, dirs, files = os.walk(pathDir).next()
file_count = len(files)
for num_exp in range(file_count):
ddf = pd.read_pickle(os.path.join('resources','results',folder,'ds_' + str(ds),'results_pipe_'
+ str(ds) + '_' + str(num_exp) + '.pkl'))
ddf["ds"] = ds
print ddf.shape
dfAll.append(ddf)
(76, 23)
(76, 23)
(76, 23)
In [303]:
dfs = pd.DataFrame(np.vstack(dfAll), columns=ddf.columns)
dfs.cv_f1_mean = pd.to_numeric(dfs.cv_f1_mean)
dfs.cv_rec_mean = pd.to_numeric(dfs.cv_rec_mean)
dfs.cv_prec_mean = pd.to_numeric(dfs.cv_prec_mean)
dfs.cv_f1_std = pd.to_numeric(dfs.cv_f1_std)
dfs.cv_prec_std = pd.to_numeric(dfs.cv_prec_std)
dfs.cv_rec_std = pd.to_numeric(dfs.cv_rec_std)
dfs.exp = pd.to_numeric(dfs.exp)
dfs["cv_f1"] = dfs["cv_f1_mean"].round(2).astype(str).str.cat(dfs["cv_f1_std"].round(2).astype(str), sep="+/-")
dfs["cv_prec"] = dfs["cv_prec_mean"].round(2).astype(str).str.cat(dfs["cv_prec_std"].round(2).astype(str), sep="+/-")
dfs["cv_rec"] = dfs["cv_rec_mean"].round(2).astype(str).str.cat(dfs["cv_rec_std"].round(2).astype(str), sep="+/-")
dfs["num_nones"] = np.sum(np.hstack(((dfs.sm == "none").reshape(-1,1),(dfs.fs == "none").reshape(-1,1))), axis=1)
print dfs.columns.tolist()
print dfs.shape
['exp', 'out', 'fs', 'sm', 'cls', 'metric', 'params', 'tr_f1', 'tr_prec', 'tr_rec', 'cv_f1_mean', 'cv_f1_std', 'cv_prec_mean', 'cv_prec_std', 'cv_rec_mean', 'cv_rec_std', 'test_f1', 'test_prec', 'test_rec', 'test_auc', 'time', 'pipeline', 'ds', 'cv_f1', 'cv_prec', 'cv_rec', 'num_nones']
(228, 27)
cv_f1 cv_prec cv_rec
0 0.52+/-0.19 0.54+/-0.22 0.54+/-0.18
1 0.55+/-0.14 0.58+/-0.2 0.59+/-0.12
2 0.47+/-0.17 0.48+/-0.2 0.5+/-0.16
3 0.47+/-0.17 0.48+/-0.2 0.5+/-0.16
4 0.5+/-0.19 0.52+/-0.19 0.51+/-0.19
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/ipykernel/__main__.py:15: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead
In [346]:
idx = []
for ds in datasets:
dfTmp = dfs[dfs.ds == ds].sort_values(["cv_f1_mean","exp"], ascending=False)
idx.extend(dfTmp.iloc[:1].index.tolist())
print idx
dfs.ix[idx][["exp","ds","sm","fs","metric","cls","params","cv_f1_mean","cv_f1_std","test_f1"]]
[50, 126, 206]
Out[346]:
exp
ds
sm
fs
metric
cls
params
cv_f1_mean
cv_f1_std
test_f1
50
50
0
none
none
f1_weighted
svmRBF
[0.001, None, 50]
0.71
0.18
0.76
126
50
1
none
none
f1_weighted
svmRBF
[0.001, None, 30]
0.83
0.08
0.84
206
54
2
after
none
f1_weighted
logReg
[None, 0.5, 3, l1]
0.90
0.12
0.85
In [325]:
#Plot performances best classifier algorithms
plt.figure(figsize=(15,6))
w = 0.20
thr = 1
errs = dfs.ix[idx]["cv_f1_std"].values
errs = np.vstack((errs, np.array([0,0,0]))).T
for i,ds in enumerate(datasets):
ax = plt.subplot(1,3,ds+1)
dfTmp = dfs[dfs.ds == ds].sort_values("cv_f1_mean", ascending=False)
dfTmp.iloc[:thr][["cv_f1_mean","test_f1"]].plot(kind="bar", yerr=[[errs[i,0]],[0.0]], rot=0, ax=ax, alpha=0.5)
print dfTmp.iloc[:thr].index.values, dfTmp.iloc[:thr]["exp"].values, dfTmp.iloc[:thr]["ds"].values
ax.set_xticks([])
ax.set_xlabel("D"+str(ds))
ax.set_ylim(0.4,1.08)
plt.legend()
plt.show()
[50] [50] [0]
[126] [50] [1]
[206] [54] [2]
In [347]:
tmp = []
for d in dfs.ds.unique():
for c in dfs.cls.unique():
tmp.append(dfs[np.logical_and(dfs.ds == d,dfs.cls == c)]
[["exp","ds","sm","fs","metric","cls","params","cv_f1_mean","cv_f1_std","cv_f1","test_f1", "pipeline"]].
sort_values(["cv_f1_mean","exp"], ascending=False).iloc[0].values)
dfTmp = pd.DataFrame(tmp, columns=["exp","ds","sm","fs","metric","cls","params","cv_f1_mean","cv_f1_std","cv_f1","test_f1", "pipeline"])
dfTmp.sort_values(["ds","cv_f1_mean"], ascending=True)[["exp","ds","sm","fs","metric","cls","params","cv_f1","test_f1"]]
Out[347]:
exp
ds
sm
fs
metric
cls
params
cv_f1
test_f1
1
59
0
after
none
f1_weighted
rf
[gini, None, None, 150, 5]
0.61+/-0.18
0.84
0
69
0
none
rfe_rf_fs
f1_weighted
knn
[21, uniform, 5, 0.1]
0.66+/-0.2
0.60
3
45
0
none
none
precision_weighted
logReg
[None, 30, l2]
0.7+/-0.16
0.76
2
50
0
none
none
f1_weighted
svmRBF
[0.001, None, 50]
0.71+/-0.18
0.76
4
48
0
none
none
f1_weighted
nn
[(30, 30), 0.01]
0.71+/-0.18
0.76
5
6
1
none
combine_fs
f1_weighted
knn
[uniform, 1, 20]
0.62+/-0.22
0.46
6
1
1
none
combine_fs
precision_weighted
rf
[100, gini, 4, balanced, 20]
0.7+/-0.1
0.54
8
55
1
after
none
precision_weighted
logReg
[None, 0.5, 3, l2]
0.76+/-0.15
0.84
9
58
1
after
none
f1_weighted
nn
[(100, 100), 4, 0.01]
0.8+/-0.11
0.84
7
50
1
none
none
f1_weighted
svmRBF
[0.001, None, 30]
0.83+/-0.08
0.84
10
16
2
after
combine_fs
f1_weighted
knn
[distance, 11, 5, 3]
0.77+/-0.09
0.85
12
53
2
after
none
precision_weighted
svmRBF
[0.001, None, 3, 30]
0.83+/-0.11
0.77
14
58
2
after
none
f1_weighted
nn
[(100,), 5, 1e-05]
0.85+/-0.08
0.84
11
39
2
none
lasso_fs
precision_weighted
rf
[entropy, 1, None, None, 500]
0.87+/-0.12
0.84
13
54
2
after
none
f1_weighted
logReg
[None, 0.5, 3, l1]
0.9+/-0.12
0.85
In [355]:
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.stats.multicomp import MultiComparison
from statsmodels.stats.libqsturng import psturng
import scipy.stats as stats
all_cvs = []
for ds in dfTmp.ds.unique():
print "\nDS:", ds
datafilenames = []
datafilenames.append(os.path.join('resources','data_partition_cl0_short_no_monit_14012016.pkl'))
datafilenames.append(os.path.join('resources','data_partition_cl0_short_1m_monit_14012016.pkl'))
datafilenames.append(os.path.join('resources','data_partition_cl0_short_3m_1m_monit_14012016.pkl'))
#Load Train & test data
f = file(datafilenames[ds],"rb")
X_train = np.load(f).astype(float)
y_train = np.load(f).astype(int)
X_test = np.load(f).astype(float)
y_test = np.load(f).astype(int)
cols = np.load(f)
tr_codes = np.load(f)
ts_codes = np.load(f)
feat_types = np.load(f)
feat_types = dict(feat_types.tolist())
f.close()
#Join data
X_all = np.vstack((X_train,X_test))
y_all = np.hstack((y_train, y_test)).reshape(-1,1)
all_codes = np.hstack((tr_codes,ts_codes))
#CV data
cv_outer = cross_validation.StratifiedShuffleSplit(y_train, n_iter=10, test_size=0.30, random_state=42)
#Compute all k-fold cv
cvs = []
for exp in dfTmp[dfTmp.ds == ds].exp.values:
dt = dfTmp[np.logical_and(dfTmp.ds == ds, dfTmp.exp == exp)]
cls = dt["pipeline"].values[0]
cv = cross_validation.cross_val_score(cls, X_train, y_train, cv=cv_outer, scoring='f1_weighted', n_jobs=-1)
for c in cv:
cvs.append([ds, exp, c])
#Compute significance (anova)
cvs = np.array(cvs)
all_cvs.append(cvs)
st, p = stats.f_oneway(*[cvs[cvs[:,1] == i,1] for i in np.unique(cvs[:,1])])
print "Pipelines:", dfTmp[dfTmp.ds == ds].exp.values
print "stat:",st, "p-value:", p
print "Means:",[np.mean(cvs[cvs[:,1] == i,1]) for i in np.unique(cvs[:,1])]
print "std:",[np.std(cvs[cvs[:,1] == i,1]) for i in np.unique(cvs[:,1])]
#Posthoc pair-wise significance tests
# http://cleverowl.uk/2015/07/01/using-one-way-anova-and-tukeys-test-to-compare-data-sets/
# https://stackoverflow.com/questions/16049552/what-statistics-module-for-python-supports-one-way-anova-with-post-hoc-tests-tu
# http://jpktd.blogspot.com.es/2013/03/multiple-comparison-and-tukey-hsd-or_25.html
mc = MultiComparison(cvs[:,2], cvs[:,1])
#Tukey test
#result = mc.tukeyhsd()
#print
#print(result)
#print(mc.groupsunique)
#T-test with bonferroni correction:
print
print mc.allpairtest(stats.ttest_rel, method='b')[0]
DS: 0
Pipelines: [69 59 50 45 48]
stat: -4.73504682502e+16 p-value: nan
Means: [45.0, 48.0, 50.0, 59.0, 69.0]
std: [0.0, 0.0, 0.0, 0.0, 0.0]
Test Multiple Comparison ttest_rel
FWER=0.05 method=b
alphacSidak=0.01, alphacBonf=0.005
=============================================
group1 group2 stat pval pval_corr reject
---------------------------------------------
45.0 48.0 0.4535 0.6609 1.0 False
45.0 50.0 0.4535 0.6609 1.0 False
45.0 59.0 1.9171 0.0875 0.8746 False
45.0 69.0 1.4525 0.1803 1.0 False
48.0 50.0 nan nan nan False
48.0 59.0 1.8263 0.1011 1.0 False
48.0 69.0 1.0345 0.3279 1.0 False
50.0 59.0 1.8263 0.1011 1.0 False
50.0 69.0 1.0345 0.3279 1.0 False
59.0 69.0 -0.7602 0.4666 1.0 False
---------------------------------------------
DS: 1
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
Pipelines: [ 6 1 50 55 58]
stat: inf p-value: 0.0
Means: [1.0, 6.0, 50.0, 55.0, 58.0]
std: [0.0, 0.0, 0.0, 0.0, 0.0]
Test Multiple Comparison ttest_rel
FWER=0.05 method=b
alphacSidak=0.01, alphacBonf=0.005
=============================================
group1 group2 stat pval pval_corr reject
---------------------------------------------
1.0 6.0 0.6406 0.5378 1.0 False
1.0 50.0 -3.5115 0.0066 0.066 False
1.0 55.0 -1.3913 0.1976 1.0 False
1.0 58.0 -2.9769 0.0155 0.1553 False
6.0 50.0 -2.5225 0.0326 0.3263 False
6.0 55.0 -1.7542 0.1133 1.0 False
6.0 58.0 -2.3683 0.042 0.4203 False
50.0 55.0 2.1989 0.0554 0.5544 False
50.0 58.0 0.8964 0.3934 1.0 False
55.0 58.0 -1.8321 0.1002 1.0 False
---------------------------------------------
DS: 2
Pipelines: [16 39 53 54 58]
stat: inf p-value: 0.0
Means: [16.0, 39.0, 53.0, 54.0, 58.0]
std: [0.0, 0.0, 0.0, 0.0, 0.0]
Test Multiple Comparison ttest_rel
FWER=0.05 method=b
alphacSidak=0.01, alphacBonf=0.005
=============================================
group1 group2 stat pval pval_corr reject
---------------------------------------------
16.0 39.0 -0.8621 0.411 1.0 False
16.0 53.0 -0.4694 0.6499 1.0 False
16.0 54.0 -1.8384 0.0992 0.9917 False
16.0 58.0 -1.3349 0.2147 1.0 False
39.0 53.0 0.5372 0.6041 1.0 False
39.0 54.0 -0.2644 0.7975 1.0 False
39.0 58.0 -0.0591 0.9542 1.0 False
53.0 54.0 -0.9216 0.3808 1.0 False
53.0 58.0 -1.1036 0.2984 1.0 False
54.0 58.0 0.3319 0.7475 1.0 False
---------------------------------------------
In [356]:
plt.figure(figsize=(10,4))
for i,colPerf in enumerate(["cv_f1_mean","test_f1"]):
ax = plt.subplot(1,2,i+1)
for c in dfTmp.cls.unique():
dfTmp[dfTmp.cls==c].plot(x="ds",y=colPerf, ax=ax,label=c)
plt.xticks(dfs.ds.unique(), dfs.ds.unique())
plt.ylim(0.4,1.0)
plt.xticks(datasets,["DS0","DS1","DS3"])
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
fancybox=True, shadow=True, ncol=3)
plt.xlabel("")
ax.set_title(colPerf)
In [357]:
dfPivot = dfTmp.pivot_table(values="cv_f1_mean",index="ds",columns="cls").reset_index()[["knn","logReg","rf","nn","svmRBF"]]
display(dfPivot)
dfErr = dfTmp.pivot_table(values="cv_f1_std",index="ds",columns="cls").reset_index()[["knn","logReg","rf","nn","svmRBF"]]
display(dfErr)
print len(dfPivot), len(dfErr)
plt.figure(figsize=(15,8))
dfPivot.plot(kind="bar", y=dfPivot.columns, yerr=dfErr,figsize=(15,8), alpha=0.3)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.xticks(datasets,["DS0","DS1","DS3"])
plt.ylim(0.0,1.05)
plt.show()
cls
knn
logReg
rf
nn
svmRBF
0
0.66
0.70
0.61
0.71
0.71
1
0.62
0.76
0.70
0.80
0.83
2
0.77
0.90
0.87
0.85
0.83
cls
knn
logReg
rf
nn
svmRBF
0
0.20
0.16
0.18
0.18
0.18
1
0.22
0.15
0.10
0.11
0.08
2
0.09
0.12
0.12
0.08
0.11
3 3
<matplotlib.figure.Figure at 0x7f8794de2b50>
In [370]:
best_exp = dfs.ix[idx]["exp"].values
print idx
print best_exp
cvs_best = []
for i,c in enumerate(all_cvs):
#print best_exp[i]
#print c[c[:,0] == best_exp[i],:]
#print np.mean(c[c[:,0] == best_exp[i],1])
cvs_best.extend(c[np.logical_and(c[:,0] == i, c[:,1] == best_exp[i]),:].tolist())
cvs_best = np.array(cvs_best)
print cvs_best.shape
st, p = stats.f_oneway(*[cvs_best[cvs_best[:,0] == i, 2] for i in np.unique(cvs_best[:,0])])
print "stat:",st, "p-value:", p
print "Means:",[np.mean(cvs_best[cvs_best[:,0] == i,2]) for i in np.unique(cvs_best[:,0])]
print "std:",[np.std(cvs_best[cvs_best[:,0] == i,2]) for c in np.unique(cvs_best[:,0])]
#Posthoc pair-wise significance tests
mc = MultiComparison(cvs_best[:,2], np.core.defchararray.add(cvs_best[:,0].astype(str),cvs_best[:,1].astype(str)),)
#T-test with bonferroni correction:
print
print mc.allpairtest(stats.ttest_rel)[0]
[50, 126, 206]
[50 50 54]
(30, 3)
stat: 3.70758492885 p-value: 0.0377821729389
Means: [0.70740259740259737, 0.82849687349687362, 0.86264790764790766]
std: [0.10265896056356723, 0.10265896056356723, 0.10265896056356723]
Test Multiple Comparison ttest_rel
FWER=0.05 method=bonf
alphacSidak=0.02, alphacBonf=0.017
===============================================
group1 group2 stat pval pval_corr reject
-----------------------------------------------
0.050.0 1.050.0 -2.4255 0.0383 0.1148 False
0.050.0 2.054.0 -3.0156 0.0146 0.0437 True
1.050.0 2.054.0 -0.7331 0.4821 1.0 False
-----------------------------------------------
In [386]:
ds = 1
In [387]:
df = dfs[dfs.ds == ds].sort_values("cv_f1_mean", ascending=False)
df.cv_f1_mean = pd.to_numeric(df.cv_f1_mean)
df.cv_rec_mean = pd.to_numeric(df.cv_rec_mean)
df.cv_prec_mean = pd.to_numeric(df.cv_prec_mean)
In [388]:
print "\nDS:", ds
datafilenames = []
datafilenames.append(os.path.join('resources','data_partition_cl0_short_no_monit_14012016.pkl'))
datafilenames.append(os.path.join('resources','data_partition_cl0_short_1m_monit_14012016.pkl'))
datafilenames.append(os.path.join('resources','data_partition_cl0_short_3m_1m_monit_14012016.pkl'))
#Load Train & test data
f = file(datafilenames[ds],"rb")
X_train = np.load(f).astype(float)
y_train = np.load(f).astype(int)
X_test = np.load(f).astype(float)
y_test = np.load(f).astype(int)
cols = np.load(f)
tr_codes = np.load(f)
ts_codes = np.load(f)
feat_types = np.load(f)
feat_types = dict(feat_types.tolist())
f.close()
#Join data
X_all = np.vstack((X_train,X_test))
y_all = np.hstack((y_train, y_test)).reshape(-1,1)
all_codes = np.hstack((tr_codes,ts_codes))
#CV data
cv_outer = cross_validation.StratifiedShuffleSplit(y_train, n_iter=10, test_size=0.30, random_state=42)
DS: 1
In [389]:
df.boxplot(by="cls",column=["cv_f1_mean"],figsize=(10,6))
plt.ylim(0.4,1.0)
plt.suptitle("")
plt.xlabel("")
plt.title("")
Out[389]:
<matplotlib.text.Text at 0x7f8788f4cd10>
In [390]:
idx = df.groupby(['cls'])['cv_f1_mean'].transform(max) == df['cv_f1_mean']
print idx[idx==True].index.tolist()
dfDescIdx = df[idx]
dfDescIdx = dfDescIdx.groupby("cls").first()
print dfDescIdx.exp.values
dfDescIdx[["fs","sm","metric","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
"test_f1",'test_prec', 'test_rec']]
[126, 119, 117, 134, 121, 131, 120, 77, 82]
[ 6 45 41 1 50]
Out[390]:
fs
sm
metric
params
tr_f1
cv_f1
cv_prec
cv_rec
test_f1
test_prec
test_rec
cls
knn
combine_fs
none
f1_weighted
[uniform, 1, 20]
1.00
0.62+/-0.22
0.63+/-0.25
0.64+/-0.2
0.46
0.46
0.47
logReg
none
none
precision_weighted
[None, 30, l2]
1.00
0.76+/-0.11
0.8+/-0.11
0.77+/-0.1
0.84
0.85
0.88
nn
none
after
precision_weighted
[(100, 100), 4, 0.01]
1.00
0.8+/-0.11
0.82+/-0.11
0.8+/-0.11
0.84
0.85
0.88
rf
combine_fs
none
precision_weighted
[100, gini, 4, balanced, 20]
1.00
0.7+/-0.1
0.76+/-0.1
0.72+/-0.09
0.54
0.54
0.54
svmRBF
none
none
f1_weighted
[0.001, None, 30]
1.00
0.83+/-0.08
0.85+/-0.07
0.83+/-0.07
0.84
0.85
0.88
CV roc curves
In [391]:
tprs = []
aucs = []
mean_fpr = np.linspace(0, 1, 9)
i = 0
plt.figure(figsize=(8,6))
ax = plt.subplot(1,1,1)
for exp in dfDescIdx.exp.values:
cls_name = dfDescIdx[dfDescIdx.exp == exp].index.values
cls = dfDescIdx[dfDescIdx.exp == exp].pipeline.iloc[0]
for train, test in cv_outer:
y_pred = cls.fit(X_train[train],y_train[train]).predict(X_train[test])
# Compute ROC curve and area under the curve
fpr, tpr, thresholds = metrics.roc_curve(y_train[test], y_pred)
tprs.append(interp(mean_fpr, fpr, tpr))
tprs[-1][0] = 0.0
roc_auc = metrics.auc(fpr, tpr)
aucs.append(roc_auc)
i += 1
#Plot auc mean line and shadow mean area
mean_auc = np.mean(aucs)
std_auc = np.std(aucs)
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
display(mean_tpr)
ax.plot(mean_fpr, mean_tpr,
label=r'Mean ROC %s (AUC = %0.2f $\pm$ %0.2f)' % (cls_name,mean_auc, std_auc),
lw=2, alpha=.8)
#Plot guess line
ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
label='', alpha=.8)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic for 10-fold CV')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
array([ 0. , 0.54166667, 0.66333333, 0.725 , 0.78666667,
0.84833333, 0.91 , 0.955 , 1. ])
array([ 0. , 0.50958333, 0.70916667, 0.76208333, 0.815 ,
0.86541667, 0.91583333, 0.95791667, 1. ])
array([ 0. , 0.51388889, 0.74111111, 0.78833333, 0.83555556,
0.87944444, 0.92333333, 0.96166667, 1. ])
array([ 0. , 0.47041667, 0.72583333, 0.79041667, 0.855 ,
0.89333333, 0.93166667, 0.96583333, 1. ])
array([ 0. , 0.47983333, 0.73966667, 0.80483333, 0.87 ,
0.90416667, 0.93833333, 0.96916667, 1. ])
Test roc curves
In [392]:
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
plt.figure(figsize=(10,6))
ax = plt.subplot(1,1,1)
for exp in dfDescIdx.exp.values:
cls_name = dfDescIdx[dfDescIdx.exp == exp].index.values
cls = dfDescIdx[dfDescIdx.exp == exp].pipeline.iloc[0]
y_pred = cls.predict(X_test)
y_prob = cls.predict_proba(X_test)
fpr_test, tpr_test, _ = metrics.roc_curve(y_test, y_prob[:,1])
roc_auc = metrics.auc(fpr_test, tpr_test)
#fpr_test_sc, tpr_test_sc, _ = metrics.roc_curve(y_test, y_score)
#roc_auc_sc = metrics.auc(fpr_test, tpr_test)
test_auc_w = metrics.roc_auc_score(y_test, y_pred, average='weighted')
print
print "DS:", ds
print exp
print cls
print y_test
print y_pred
print y_prob[:,1]
print roc_auc
print test_auc_w
print fpr_test
print tpr_test
ax.plot(fpr_test, tpr_test, label='ROC testSet %s (AUC = %0.2f)' % (cls_name, roc_auc),lw=2, alpha=.8)
ax.legend(loc='lower right')
ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',label='', alpha=.3)
plt.show()
DS: 1
6
Pipeline(steps=[('Imputer', TypeFeatImputer(allNameCols=array(['GENDER', 'ACTIVE', 'RETIRED', 'AGE', 'SMOKER', 'SMOKE_PACK_YEAR',
'SMOKE_EX_YEAR', 'ALCOHOL', 'ALCOHOL_GR_DAY', 'CAFES_DAY',
'DEPRESSION', 'ANXIETY', 'HTA', 'CARDIOPATHY', 'RESP_DIS',
'DIABETES', 'OBESITY', 'DISLIPEMIA', 'OB...owski',
metric_params=None, n_jobs=1, n_neighbors=1, p=2,
weights='uniform'))])
[0 1 1 1 0 0 0 0 1 0 1 1 1]
[1 1 0 1 1 0 0 1 1 0 1 0 1]
[ 1. 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 1.]
0.607142857143
0.607142857143
[ 0. 0.5 1. ]
[ 0. 0.71428571 1. ]
DS: 1
45
Pipeline(steps=[('Imputer', TypeFeatImputer(allNameCols=array(['GENDER', 'ACTIVE', 'RETIRED', 'AGE', 'SMOKER', 'SMOKE_PACK_YEAR',
'SMOKE_EX_YEAR', 'ALCOHOL', 'ALCOHOL_GR_DAY', 'CAFES_DAY',
'DEPRESSION', 'ANXIETY', 'HTA', 'CARDIOPATHY', 'RESP_DIS',
'DIABETES', 'OBESITY', 'DISLIPEMIA', 'OB...alty='l2', random_state=42, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False))])
[0 1 1 1 0 0 0 0 1 0 1 1 1]
[0 1 1 1 1 0 0 0 1 0 1 0 0]
[ 0.00247262 0.97558447 0.92307559 0.95686749 0.62644929 0.08257369
0.05549734 0.02571307 0.71911365 0.00887739 0.99999671 0.00419745
0.02033815]
0.785714285714
0.77380952381
[ 0. 0. 0.66666667 0.66666667 0.83333333 0.83333333
1. ]
[ 0.14285714 0.71428571 0.71428571 0.85714286 0.85714286 1. 1. ]
DS: 1
41
Pipeline(steps=[('Imputer', TypeFeatImputer(allNameCols=array(['GENDER', 'ACTIVE', 'RETIRED', 'AGE', 'SMOKER', 'SMOKE_PACK_YEAR',
'SMOKE_EX_YEAR', 'ALCOHOL', 'ALCOHOL_GR_DAY', 'CAFES_DAY',
'DEPRESSION', 'ANXIETY', 'HTA', 'CARDIOPATHY', 'RESP_DIS',
'DIABETES', 'OBESITY', 'DISLIPEMIA', 'OB... solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False))])
[0 1 1 1 0 0 0 0 1 0 1 1 1]
[0 1 1 1 1 0 0 0 1 0 1 0 0]
[ 0.00187182 0.99015314 0.97225311 0.98062276 0.61225805 0.0379287
0.03211616 0.01722012 0.70515126 0.00405211 0.99998008 0.00182539
0.01055821]
0.761904761905
0.77380952381
[ 0. 0. 0.66666667 0.66666667 1. 1. ]
[ 0.14285714 0.71428571 0.71428571 0.85714286 0.85714286 1. ]
DS: 1
1
Pipeline(steps=[('Imputer', TypeFeatImputer(allNameCols=array(['GENDER', 'ACTIVE', 'RETIRED', 'AGE', 'SMOKER', 'SMOKE_PACK_YEAR',
'SMOKE_EX_YEAR', 'ALCOHOL', 'ALCOHOL_GR_DAY', 'CAFES_DAY',
'DEPRESSION', 'ANXIETY', 'HTA', 'CARDIOPATHY', 'RESP_DIS',
'DIABETES', 'OBESITY', 'DISLIPEMIA', 'OB...imators=100, n_jobs=-1,
oob_score=False, random_state=42, verbose=0, warm_start=False))])
[0 1 1 1 0 0 0 0 1 0 1 1 1]
[0 1 1 1 0 1 0 1 1 0 1 1 0]
[ 0.31 0.82 0.74 0.88 0.48181818 0.57 0.4
0.73 0.79 0.38 0.93 0.52 0.24 ]
0.809523809524
0.761904761905
[ 0. 0. 0.33333333 0.33333333 1. 1. ]
[ 0.14285714 0.71428571 0.71428571 0.85714286 0.85714286 1. ]
DS: 1
50
Pipeline(steps=[('Imputer', TypeFeatImputer(allNameCols=array(['GENDER', 'ACTIVE', 'RETIRED', 'AGE', 'SMOKER', 'SMOKE_PACK_YEAR',
'SMOKE_EX_YEAR', 'ALCOHOL', 'ALCOHOL_GR_DAY', 'CAFES_DAY',
'DEPRESSION', 'ANXIETY', 'HTA', 'CARDIOPATHY', 'RESP_DIS',
'DIABETES', 'OBESITY', 'DISLIPEMIA', 'OB...bf',
max_iter=-1, probability=True, random_state=42, shrinking=True,
tol=0.001, verbose=False))])
[0 1 1 1 0 0 0 0 1 0 1 1 1]
[0 1 1 1 1 0 0 0 1 0 1 0 0]
[ 0.51181977 0.6468261 0.63357833 0.63786607 0.60179588 0.56306431
0.55039371 0.54299657 0.60592509 0.52004931 0.73916563 0.51286223
0.53535692]
0.785714285714
0.77380952381
[ 0. 0. 0.66666667 0.66666667 0.83333333 0.83333333
1. ]
[ 0.14285714 0.71428571 0.71428571 0.85714286 0.85714286 1. 1. ]
In [393]:
#Compute all k-fold cv
cvs = []
for exp in df.exp.values:
dt = df[df.exp == exp]
cls = dt["pipeline"].values[0]
cv = cross_validation.cross_val_score(cls, X_train, y_train, cv=cv_outer, scoring='f1_weighted', n_jobs=-1)
for c in cv:
cvs.append([exp, c])
#Compute significance (anova)
cvs = np.array(cvs)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/aegle/miniconda2/envs/myosa/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
In [394]:
#Multiple tests
st, p = stats.f_oneway(*[cvs[cvs[:,0] == i,1] for i in np.unique(cvs[:,0])])
print "stat:",st, "p-value:", p
#Find pairwise adhoc significance of dependent tests
mc = MultiComparison(cvs[:,1], cvs[:,0])
#Tukey test
result = mc.tukeyhsd()
print(result)
print(mc.groupsunique)
#T-test with bonferroni correction:
print
print mc.allpairtest(stats.ttest_rel,method='b')[0]
stat: 2.07665146572 p-value: 1.16055190762e-06
Multiple Comparison of Means - Tukey HSD,FWER=0.05
============================================
group1 group2 meandiff lower upper reject
--------------------------------------------
0.0 1.0 0.0808 -0.2854 0.447 False
0.0 2.0 -0.049 -0.4152 0.3172 False
0.0 3.0 -0.0193 -0.3855 0.3469 False
0.0 4.0 0.0722 -0.294 0.4384 False
0.0 5.0 0.0722 -0.294 0.4384 False
0.0 6.0 0.0205 -0.3457 0.3867 False
0.0 7.0 -0.0795 -0.4458 0.2867 False
0.0 8.0 -0.0588 -0.425 0.3074 False
0.0 9.0 0.0686 -0.2976 0.4348 False
0.0 10.0 -0.0548 -0.421 0.3114 False
0.0 11.0 -0.0548 -0.421 0.3114 False
0.0 12.0 0.05 -0.3162 0.4162 False
0.0 13.0 0.0291 -0.3371 0.3953 False
0.0 14.0 0.0093 -0.3569 0.3755 False
0.0 15.0 0.0332 -0.333 0.3995 False
0.0 16.0 0.0 -0.3662 0.3662 False
0.0 17.0 -0.057 -0.4232 0.3092 False
0.0 18.0 -0.0583 -0.4245 0.3079 False
0.0 19.0 -0.0574 -0.4237 0.3088 False
0.0 20.0 -0.0025 -0.3687 0.3638 False
0.0 21.0 -0.0508 -0.417 0.3154 False
0.0 22.0 0.0595 -0.3067 0.4257 False
0.0 23.0 0.0077 -0.3585 0.3739 False
0.0 24.0 -0.0351 -0.4013 0.3311 False
0.0 25.0 -0.0701 -0.4363 0.2961 False
0.0 26.0 0.0129 -0.3533 0.3791 False
0.0 27.0 0.0129 -0.3533 0.3791 False
0.0 28.0 -0.063 -0.4292 0.3033 False
0.0 29.0 -0.0052 -0.3714 0.361 False
0.0 30.0 0.0105 -0.3557 0.3768 False
0.0 31.0 -0.0257 -0.3919 0.3405 False
0.0 32.0 0.0242 -0.342 0.3905 False
0.0 33.0 0.0122 -0.354 0.3784 False
0.0 34.0 -0.0303 -0.3965 0.3359 False
0.0 35.0 -0.0278 -0.394 0.3384 False
0.0 36.0 -0.0295 -0.3957 0.3367 False
0.0 37.0 -0.0295 -0.3957 0.3367 False
0.0 38.0 -0.0025 -0.3687 0.3638 False
0.0 39.0 -0.0508 -0.417 0.3154 False
0.0 40.0 -0.034 -0.4002 0.3322 False
0.0 41.0 0.1955 -0.1707 0.5617 False
0.0 42.0 -0.034 -0.4002 0.3322 False
0.0 43.0 0.214 -0.1522 0.5802 False
0.0 44.0 0.1595 -0.2067 0.5257 False
0.0 45.0 0.1595 -0.2067 0.5257 False
0.0 46.0 -0.1184 -0.4846 0.2478 False
0.0 47.0 -0.0752 -0.4414 0.291 False
0.0 48.0 0.1469 -0.2193 0.5131 False
0.0 49.0 0.1812 -0.185 0.5474 False
0.0 50.0 0.214 -0.1522 0.5802 False
0.0 51.0 0.0591 -0.3071 0.4253 False
0.0 52.0 0.214 -0.1522 0.5802 False
0.0 53.0 0.214 -0.1522 0.5802 False
0.0 54.0 0.1373 -0.2289 0.5035 False
0.0 55.0 0.1473 -0.2189 0.5135 False
0.0 56.0 -0.1085 -0.4747 0.2577 False
0.0 57.0 -0.1085 -0.4747 0.2577 False
0.0 58.0 0.1955 -0.1707 0.5617 False
0.0 59.0 0.0591 -0.3071 0.4253 False
0.0 60.0 -0.1333 -0.4996 0.2329 False
0.0 61.0 -0.1333 -0.4996 0.2329 False
0.0 62.0 0.057 -0.3092 0.4232 False
0.0 63.0 0.0875 -0.2787 0.4537 False
0.0 64.0 0.0544 -0.3118 0.4206 False
0.0 65.0 0.0791 -0.2871 0.4453 False
0.0 66.0 0.0522 -0.314 0.4184 False
0.0 67.0 0.0334 -0.3328 0.3996 False
0.0 68.0 -0.0602 -0.4264 0.306 False
0.0 69.0 -0.0602 -0.4264 0.306 False
0.0 70.0 0.1034 -0.2628 0.4696 False
0.0 71.0 0.0544 -0.3118 0.4206 False
0.0 72.0 0.1045 -0.2617 0.4707 False
0.0 73.0 0.0531 -0.3131 0.4194 False
0.0 74.0 0.0522 -0.314 0.4184 False
0.0 75.0 0.059 -0.3072 0.4252 False
1.0 2.0 -0.1299 -0.4961 0.2363 False
1.0 3.0 -0.1001 -0.4664 0.2661 False
1.0 4.0 -0.0086 -0.3748 0.3576 False
1.0 5.0 -0.0086 -0.3748 0.3576 False
1.0 6.0 -0.0603 -0.4265 0.3059 False
1.0 7.0 -0.1604 -0.5266 0.2058 False
1.0 8.0 -0.1396 -0.5058 0.2266 False
1.0 9.0 -0.0122 -0.3784 0.354 False
1.0 10.0 -0.1356 -0.5018 0.2306 False
1.0 11.0 -0.1356 -0.5018 0.2306 False
1.0 12.0 -0.0308 -0.397 0.3354 False
1.0 13.0 -0.0518 -0.418 0.3144 False
1.0 14.0 -0.0716 -0.4378 0.2947 False
1.0 15.0 -0.0476 -0.4138 0.3186 False
1.0 16.0 -0.0808 -0.447 0.2854 False
1.0 17.0 -0.1378 -0.504 0.2284 False
1.0 18.0 -0.1391 -0.5053 0.2271 False
1.0 19.0 -0.1383 -0.5045 0.2279 False
1.0 20.0 -0.0833 -0.4495 0.2829 False
1.0 21.0 -0.1316 -0.4978 0.2346 False
1.0 22.0 -0.0213 -0.3875 0.3449 False
1.0 23.0 -0.0731 -0.4393 0.2931 False
1.0 24.0 -0.1159 -0.4821 0.2503 False
1.0 25.0 -0.1509 -0.5171 0.2153 False
1.0 26.0 -0.0679 -0.4342 0.2983 False
1.0 27.0 -0.0679 -0.4342 0.2983 False
1.0 28.0 -0.1438 -0.51 0.2224 False
1.0 29.0 -0.086 -0.4522 0.2802 False
1.0 30.0 -0.0703 -0.4365 0.2959 False
1.0 31.0 -0.1065 -0.4727 0.2597 False
1.0 32.0 -0.0566 -0.4228 0.3096 False
1.0 33.0 -0.0686 -0.4348 0.2976 False
1.0 34.0 -0.1111 -0.4773 0.2551 False
1.0 35.0 -0.1086 -0.4748 0.2576 False
1.0 36.0 -0.1103 -0.4765 0.2559 False
1.0 37.0 -0.1103 -0.4765 0.2559 False
1.0 38.0 -0.0833 -0.4495 0.2829 False
1.0 39.0 -0.1316 -0.4978 0.2346 False
1.0 40.0 -0.1148 -0.481 0.2514 False
1.0 41.0 0.1146 -0.2516 0.4808 False
1.0 42.0 -0.1148 -0.481 0.2514 False
1.0 43.0 0.1332 -0.233 0.4994 False
1.0 44.0 0.0787 -0.2875 0.4449 False
1.0 45.0 0.0787 -0.2875 0.4449 False
1.0 46.0 -0.1992 -0.5654 0.167 False
1.0 47.0 -0.1561 -0.5223 0.2101 False
1.0 48.0 0.066 -0.3002 0.4322 False
1.0 49.0 0.1004 -0.2659 0.4666 False
1.0 50.0 0.1332 -0.233 0.4994 False
1.0 51.0 -0.0217 -0.388 0.3445 False
1.0 52.0 0.1332 -0.233 0.4994 False
1.0 53.0 0.1332 -0.233 0.4994 False
1.0 54.0 0.0565 -0.3097 0.4227 False
1.0 55.0 0.0665 -0.2997 0.4327 False
1.0 56.0 -0.1894 -0.5556 0.1768 False
1.0 57.0 -0.1894 -0.5556 0.1768 False
1.0 58.0 0.1146 -0.2516 0.4808 False
1.0 59.0 -0.0217 -0.388 0.3445 False
1.0 60.0 -0.2142 -0.5804 0.152 False
1.0 61.0 -0.2142 -0.5804 0.152 False
1.0 62.0 -0.0238 -0.39 0.3424 False
1.0 63.0 0.0067 -0.3595 0.3729 False
1.0 64.0 -0.0264 -0.3926 0.3398 False
1.0 65.0 -0.0017 -0.368 0.3645 False
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59.0 69.0 -0.1193 -0.4855 0.2469 False
59.0 70.0 0.0443 -0.3219 0.4106 False
59.0 71.0 -0.0047 -0.3709 0.3615 False
59.0 72.0 0.0455 -0.3208 0.4117 False
59.0 73.0 -0.0059 -0.3721 0.3603 False
59.0 74.0 -0.0069 -0.3731 0.3593 False
59.0 75.0 -0.0001 -0.3663 0.3661 False
60.0 61.0 0.0 -0.3662 0.3662 False
60.0 62.0 0.1903 -0.1759 0.5566 False
60.0 63.0 0.2209 -0.1454 0.5871 False
60.0 64.0 0.1877 -0.1785 0.554 False
60.0 65.0 0.2124 -0.1538 0.5786 False
60.0 66.0 0.1855 -0.1807 0.5517 False
60.0 67.0 0.1667 -0.1995 0.5329 False
60.0 68.0 0.0731 -0.2931 0.4393 False
60.0 69.0 0.0731 -0.2931 0.4393 False
60.0 70.0 0.2368 -0.1294 0.603 False
60.0 71.0 0.1877 -0.1785 0.554 False
60.0 72.0 0.2379 -0.1283 0.6041 False
60.0 73.0 0.1865 -0.1797 0.5527 False
60.0 74.0 0.1855 -0.1807 0.5517 False
60.0 75.0 0.1923 -0.1739 0.5585 False
61.0 62.0 0.1903 -0.1759 0.5566 False
61.0 63.0 0.2209 -0.1454 0.5871 False
61.0 64.0 0.1877 -0.1785 0.554 False
61.0 65.0 0.2124 -0.1538 0.5786 False
61.0 66.0 0.1855 -0.1807 0.5517 False
61.0 67.0 0.1667 -0.1995 0.5329 False
61.0 68.0 0.0731 -0.2931 0.4393 False
61.0 69.0 0.0731 -0.2931 0.4393 False
61.0 70.0 0.2368 -0.1294 0.603 False
61.0 71.0 0.1877 -0.1785 0.554 False
61.0 72.0 0.2379 -0.1283 0.6041 False
61.0 73.0 0.1865 -0.1797 0.5527 False
61.0 74.0 0.1855 -0.1807 0.5517 False
61.0 75.0 0.1923 -0.1739 0.5585 False
62.0 63.0 0.0305 -0.3357 0.3967 False
62.0 64.0 -0.0026 -0.3688 0.3636 False
62.0 65.0 0.0221 -0.3441 0.3883 False
62.0 66.0 -0.0048 -0.371 0.3614 False
62.0 67.0 -0.0236 -0.3898 0.3426 False
62.0 68.0 -0.1172 -0.4834 0.249 False
62.0 69.0 -0.1172 -0.4834 0.249 False
62.0 70.0 0.0464 -0.3198 0.4126 False
62.0 71.0 -0.0026 -0.3688 0.3636 False
62.0 72.0 0.0475 -0.3187 0.4137 False
62.0 73.0 -0.0039 -0.3701 0.3624 False
62.0 74.0 -0.0048 -0.371 0.3614 False
62.0 75.0 0.002 -0.3642 0.3682 False
63.0 64.0 -0.0331 -0.3993 0.3331 False
63.0 65.0 -0.0084 -0.3746 0.3578 False
63.0 66.0 -0.0353 -0.4015 0.3309 False
63.0 67.0 -0.0541 -0.4203 0.3121 False
63.0 68.0 -0.1477 -0.5139 0.2185 False
63.0 69.0 -0.1477 -0.5139 0.2185 False
63.0 70.0 0.0159 -0.3503 0.3821 False
63.0 71.0 -0.0331 -0.3993 0.3331 False
63.0 72.0 0.017 -0.3492 0.3832 False
63.0 73.0 -0.0344 -0.4006 0.3318 False
63.0 74.0 -0.0353 -0.4015 0.3309 False
63.0 75.0 -0.0285 -0.3947 0.3377 False
64.0 65.0 0.0247 -0.3415 0.3909 False
64.0 66.0 -0.0022 -0.3684 0.364 False
64.0 67.0 -0.021 -0.3872 0.3452 False
64.0 68.0 -0.1146 -0.4808 0.2516 False
64.0 69.0 -0.1146 -0.4808 0.2516 False
64.0 70.0 0.049 -0.3172 0.4152 False
64.0 71.0 0.0 -0.3662 0.3662 False
64.0 72.0 0.0501 -0.3161 0.4163 False
64.0 73.0 -0.0013 -0.3675 0.365 False
64.0 74.0 -0.0022 -0.3684 0.364 False
64.0 75.0 0.0046 -0.3616 0.3708 False
65.0 66.0 -0.0269 -0.3931 0.3393 False
65.0 67.0 -0.0457 -0.4119 0.3205 False
65.0 68.0 -0.1393 -0.5055 0.2269 False
65.0 69.0 -0.1393 -0.5055 0.2269 False
65.0 70.0 0.0243 -0.3419 0.3905 False
65.0 71.0 -0.0247 -0.3909 0.3415 False
65.0 72.0 0.0255 -0.3408 0.3917 False
65.0 73.0 -0.0259 -0.3921 0.3403 False
65.0 74.0 -0.0269 -0.3931 0.3393 False
65.0 75.0 -0.0201 -0.3863 0.3461 False
66.0 67.0 -0.0188 -0.385 0.3474 False
66.0 68.0 -0.1124 -0.4786 0.2538 False
66.0 69.0 -0.1124 -0.4786 0.2538 False
66.0 70.0 0.0512 -0.315 0.4175 False
66.0 71.0 0.0022 -0.364 0.3684 False
66.0 72.0 0.0524 -0.3138 0.4186 False
66.0 73.0 0.001 -0.3652 0.3672 False
66.0 74.0 0.0 -0.3662 0.3662 False
66.0 75.0 0.0068 -0.3594 0.373 False
67.0 68.0 -0.0936 -0.4598 0.2726 False
67.0 69.0 -0.0936 -0.4598 0.2726 False
67.0 70.0 0.0701 -0.2962 0.4363 False
67.0 71.0 0.021 -0.3452 0.3872 False
67.0 72.0 0.0712 -0.295 0.4374 False
67.0 73.0 0.0198 -0.3464 0.386 False
67.0 74.0 0.0188 -0.3474 0.385 False
67.0 75.0 0.0256 -0.3406 0.3918 False
68.0 69.0 0.0 -0.3662 0.3662 False
68.0 70.0 0.1636 -0.2026 0.5299 False
68.0 71.0 0.1146 -0.2516 0.4808 False
68.0 72.0 0.1648 -0.2015 0.531 False
68.0 73.0 0.1134 -0.2528 0.4796 False
68.0 74.0 0.1124 -0.2538 0.4786 False
68.0 75.0 0.1192 -0.247 0.4854 False
69.0 70.0 0.1636 -0.2026 0.5299 False
69.0 71.0 0.1146 -0.2516 0.4808 False
69.0 72.0 0.1648 -0.2015 0.531 False
69.0 73.0 0.1134 -0.2528 0.4796 False
69.0 74.0 0.1124 -0.2538 0.4786 False
69.0 75.0 0.1192 -0.247 0.4854 False
70.0 71.0 -0.049 -0.4152 0.3172 False
70.0 72.0 0.0011 -0.3651 0.3673 False
70.0 73.0 -0.0503 -0.4165 0.3159 False
70.0 74.0 -0.0512 -0.4175 0.315 False
70.0 75.0 -0.0444 -0.4107 0.3218 False
71.0 72.0 0.0501 -0.3161 0.4163 False
71.0 73.0 -0.0013 -0.3675 0.365 False
71.0 74.0 -0.0022 -0.3684 0.364 False
71.0 75.0 0.0046 -0.3616 0.3708 False
72.0 73.0 -0.0514 -0.4176 0.3148 False
72.0 74.0 -0.0524 -0.4186 0.3138 False
72.0 75.0 -0.0456 -0.4118 0.3207 False
73.0 74.0 -0.001 -0.3672 0.3652 False
73.0 75.0 0.0058 -0.3604 0.372 False
74.0 75.0 0.0068 -0.3594 0.373 False
--------------------------------------------
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.
30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44.
45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59.
60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74.
75.]
Test Multiple Comparison ttest_rel
FWER=0.05 method=b
alphacSidak=0.00, alphacBonf=0.000
==============================================
group1 group2 stat pval pval_corr reject
----------------------------------------------
0.0 1.0 -1.782 0.1084 1.0 False
0.0 2.0 1.281 0.2322 1.0 False
0.0 3.0 0.3403 0.7414 1.0 False
0.0 4.0 -1.7523 0.1136 1.0 False
0.0 5.0 -1.7523 0.1136 1.0 False
0.0 6.0 -0.267 0.7955 1.0 False
0.0 7.0 1.3183 0.22 1.0 False
0.0 8.0 1.0026 0.3423 1.0 False
0.0 9.0 -1.2038 0.2594 1.0 False
0.0 10.0 0.8528 0.4159 1.0 False
0.0 11.0 0.8528 0.4159 1.0 False
0.0 12.0 -1.1734 0.2707 1.0 False
0.0 13.0 -0.5525 0.594 1.0 False
0.0 14.0 -0.1481 0.8855 1.0 False
0.0 15.0 -0.9628 0.3608 1.0 False
0.0 16.0 nan nan nan False
0.0 17.0 0.9903 0.3479 1.0 False
0.0 18.0 1.1264 0.2891 1.0 False
0.0 19.0 0.9867 0.3496 1.0 False
0.0 20.0 0.0304 0.9764 1.0 False
0.0 21.0 1.5888 0.1466 1.0 False
0.0 22.0 -1.1344 0.2859 1.0 False
0.0 23.0 -0.1555 0.8798 1.0 False
0.0 24.0 0.6791 0.5142 1.0 False
0.0 25.0 1.3969 0.1959 1.0 False
0.0 26.0 -0.1677 0.8705 1.0 False
0.0 27.0 -0.1677 0.8705 1.0 False
0.0 28.0 1.1163 0.2932 1.0 False
0.0 29.0 0.0843 0.9347 1.0 False
0.0 30.0 -0.4051 0.6949 1.0 False
0.0 31.0 1.0426 0.3243 1.0 False
0.0 32.0 -0.4386 0.6713 1.0 False
0.0 33.0 -0.2337 0.8204 1.0 False
0.0 34.0 0.4984 0.6302 1.0 False
0.0 35.0 0.6146 0.5541 1.0 False
0.0 36.0 0.5106 0.6219 1.0 False
0.0 37.0 0.5106 0.6219 1.0 False
0.0 38.0 0.0304 0.9764 1.0 False
0.0 39.0 1.5888 0.1466 1.0 False
0.0 40.0 0.7899 0.4499 1.0 False
0.0 41.0 -5.5537 0.0004 1.0 False
0.0 42.0 0.8364 0.4246 1.0 False
0.0 43.0 -5.0682 0.0007 1.0 False
0.0 44.0 -4.4151 0.0017 1.0 False
0.0 45.0 -4.4151 0.0017 1.0 False
0.0 46.0 2.361 0.0425 1.0 False
0.0 47.0 1.7416 0.1156 1.0 False
0.0 48.0 -3.299 0.0092 1.0 False
0.0 49.0 -4.9837 0.0008 1.0 False
0.0 50.0 -5.0682 0.0007 1.0 False
0.0 51.0 -1.8634 0.0953 1.0 False
0.0 52.0 -5.0682 0.0007 1.0 False
0.0 53.0 -5.0682 0.0007 1.0 False
0.0 54.0 -4.8988 0.0008 1.0 False
0.0 55.0 -3.39 0.008 1.0 False
0.0 56.0 2.4208 0.0386 1.0 False
0.0 57.0 2.4208 0.0386 1.0 False
0.0 58.0 -5.5537 0.0004 1.0 False
0.0 59.0 -1.8634 0.0953 1.0 False
0.0 60.0 2.5086 0.0334 1.0 False
0.0 61.0 2.5086 0.0334 1.0 False
0.0 62.0 -0.8737 0.405 1.0 False
0.0 63.0 -1.7721 0.1101 1.0 False
0.0 64.0 -0.8653 0.4094 1.0 False
0.0 65.0 -1.1838 0.2668 1.0 False
0.0 66.0 -0.7031 0.4998 1.0 False
0.0 67.0 -0.5762 0.5786 1.0 False
0.0 68.0 0.7393 0.4786 1.0 False
0.0 69.0 0.7393 0.4786 1.0 False
0.0 70.0 -2.4377 0.0375 1.0 False
0.0 71.0 -0.8653 0.4094 1.0 False
0.0 72.0 -1.8247 0.1013 1.0 False
0.0 73.0 -0.8592 0.4125 1.0 False
0.0 74.0 -0.7031 0.4998 1.0 False
0.0 75.0 -0.8533 0.4156 1.0 False
1.0 2.0 2.4033 0.0397 1.0 False
1.0 3.0 1.874 0.0937 1.0 False
1.0 4.0 0.1298 0.8996 1.0 False
1.0 5.0 0.1298 0.8996 1.0 False
1.0 6.0 0.6406 0.5378 1.0 False
1.0 7.0 2.5643 0.0305 1.0 False
1.0 8.0 2.2601 0.0502 1.0 False
1.0 9.0 0.1631 0.8741 1.0 False
1.0 10.0 2.5279 0.0323 1.0 False
1.0 11.0 2.5279 0.0323 1.0 False
1.0 12.0 0.6638 0.5235 1.0 False
1.0 13.0 0.7883 0.4508 1.0 False
1.0 14.0 0.8335 0.4261 1.0 False
1.0 15.0 0.9 0.3916 1.0 False
1.0 16.0 1.782 0.1084 1.0 False
1.0 17.0 1.9256 0.0863 1.0 False
1.0 18.0 2.0838 0.0668 1.0 False
1.0 19.0 2.0698 0.0684 1.0 False
1.0 20.0 0.9886 0.3487 1.0 False
1.0 21.0 2.5932 0.0291 1.0 False
1.0 22.0 0.3724 0.7182 1.0 False
1.0 23.0 1.0244 0.3324 1.0 False
1.0 24.0 1.8259 0.1012 1.0 False
1.0 25.0 2.2768 0.0488 1.0 False
1.0 26.0 0.8501 0.4173 1.0 False
1.0 27.0 0.8501 0.4173 1.0 False
1.0 28.0 2.4337 0.0378 1.0 False
1.0 29.0 1.2354 0.248 1.0 False
1.0 30.0 1.5333 0.1596 1.0 False
1.0 31.0 2.1834 0.0569 1.0 False
1.0 32.0 0.8781 0.4027 1.0 False
1.0 33.0 1.1569 0.2771 1.0 False
1.0 34.0 1.8768 0.0933 1.0 False
1.0 35.0 1.8914 0.0911 1.0 False
1.0 36.0 1.4641 0.1772 1.0 False
1.0 37.0 1.4641 0.1772 1.0 False
1.0 38.0 0.9886 0.3487 1.0 False
1.0 39.0 2.5932 0.0291 1.0 False
1.0 40.0 1.8683 0.0946 1.0 False
1.0 41.0 -2.9769 0.0155 1.0 False
1.0 42.0 2.3236 0.0452 1.0 False
1.0 43.0 -3.5115 0.0066 1.0 False
1.0 44.0 -1.7852 0.1079 1.0 False
1.0 45.0 -1.7852 0.1079 1.0 False
1.0 46.0 2.8905 0.0179 1.0 False
1.0 47.0 2.9674 0.0158 1.0 False
1.0 48.0 -1.1788 0.2687 1.0 False
1.0 49.0 -1.754 0.1133 1.0 False
1.0 50.0 -3.5115 0.0066 1.0 False
1.0 51.0 0.522 0.6142 1.0 False
1.0 52.0 -3.5115 0.0066 1.0 False
1.0 53.0 -3.5115 0.0066 1.0 False
1.0 54.0 -1.2767 0.2337 1.0 False
1.0 55.0 -1.3913 0.1976 1.0 False
1.0 56.0 2.8391 0.0194 1.0 False
1.0 57.0 2.8391 0.0194 1.0 False
1.0 58.0 -2.9769 0.0155 1.0 False
1.0 59.0 0.522 0.6142 1.0 False
1.0 60.0 3.9224 0.0035 1.0 False
1.0 61.0 3.9224 0.0035 1.0 False
1.0 62.0 0.3105 0.7633 1.0 False
1.0 63.0 -0.1004 0.9222 1.0 False
1.0 64.0 0.3507 0.7339 1.0 False
1.0 65.0 0.0233 0.9819 1.0 False
1.0 66.0 0.3013 0.77 1.0 False
1.0 67.0 0.6527 0.5302 1.0 False
1.0 68.0 1.5577 0.1537 1.0 False
1.0 69.0 1.5577 0.1537 1.0 False
1.0 70.0 -0.3535 0.7319 1.0 False
1.0 71.0 0.3507 0.7339 1.0 False
1.0 72.0 -0.3645 0.7239 1.0 False
1.0 73.0 0.3716 0.7188 1.0 False
1.0 74.0 0.3013 0.77 1.0 False
1.0 75.0 0.2531 0.8059 1.0 False
2.0 3.0 -0.6886 0.5084 1.0 False
2.0 4.0 -3.1103 0.0125 1.0 False
2.0 5.0 -3.1103 0.0125 1.0 False
2.0 6.0 -0.9473 0.3682 1.0 False
2.0 7.0 0.4973 0.6309 1.0 False
2.0 8.0 0.1562 0.8793 1.0 False
2.0 9.0 -1.485 0.1717 1.0 False
2.0 10.0 0.1144 0.9114 1.0 False
2.0 11.0 0.1144 0.9114 1.0 False
2.0 12.0 -2.1837 0.0568 1.0 False
2.0 13.0 -1.0773 0.3094 1.0 False
2.0 14.0 -0.9767 0.3543 1.0 False
2.0 15.0 -1.9031 0.0894 1.0 False
2.0 16.0 -1.281 0.2322 1.0 False
2.0 17.0 0.1564 0.8792 1.0 False
2.0 18.0 0.1756 0.8645 1.0 False
2.0 19.0 0.1884 0.8547 1.0 False
2.0 20.0 -0.4975 0.6308 1.0 False
2.0 21.0 0.04 0.969 1.0 False
2.0 22.0 -1.5471 0.1563 1.0 False
2.0 23.0 -0.8668 0.4085 1.0 False
2.0 24.0 -0.181 0.8604 1.0 False
2.0 25.0 0.2651 0.7969 1.0 False
2.0 26.0 -0.9019 0.3906 1.0 False
2.0 27.0 -0.9019 0.3906 1.0 False
2.0 28.0 0.1772 0.8633 1.0 False
2.0 29.0 -0.6071 0.5588 1.0 False
2.0 30.0 -1.2595 0.2395 1.0 False
2.0 31.0 -0.5351 0.6055 1.0 False
2.0 32.0 -1.1426 0.2827 1.0 False
2.0 33.0 -0.8937 0.3948 1.0 False
2.0 34.0 -0.2434 0.8131 1.0 False
2.0 35.0 -0.3363 0.7444 1.0 False
2.0 36.0 -0.3739 0.7172 1.0 False
2.0 37.0 -0.3739 0.7172 1.0 False
2.0 38.0 -0.4975 0.6308 1.0 False
2.0 39.0 0.04 0.969 1.0 False
2.0 40.0 -0.2698 0.7934 1.0 False
2.0 41.0 -5.8428 0.0002 0.7009 False
2.0 42.0 -0.3264 0.7516 1.0 False
2.0 43.0 -6.5168 0.0001 0.3115 False
2.0 44.0 -6.8441 0.0001 0.2144 False
2.0 45.0 -6.8441 0.0001 0.2144 False
2.0 46.0 1.9002 0.0899 1.0 False
2.0 47.0 0.5151 0.6189 1.0 False
2.0 48.0 -4.1523 0.0025 1.0 False
2.0 49.0 -5.8699 0.0002 0.6777 False
2.0 50.0 -6.5168 0.0001 0.3115 False
2.0 51.0 -2.9369 0.0166 1.0 False
2.0 52.0 -6.5168 0.0001 0.3115 False
2.0 53.0 -6.5168 0.0001 0.3115 False
2.0 54.0 -6.0663 0.0002 0.5322 False
2.0 55.0 -5.571 0.0003 0.9888 False
2.0 56.0 0.7997 0.4445 1.0 False
2.0 57.0 0.7997 0.4445 1.0 False
2.0 58.0 -5.8428 0.0002 0.7009 False
2.0 59.0 -2.9369 0.0166 1.0 False
2.0 60.0 1.4346 0.1852 1.0 False
2.0 61.0 1.4346 0.1852 1.0 False
2.0 62.0 -1.5332 0.1596 1.0 False
2.0 63.0 -2.9087 0.0173 1.0 False
2.0 64.0 -1.6815 0.127 1.0 False
2.0 65.0 -1.9689 0.0805 1.0 False
2.0 66.0 -1.2837 0.2313 1.0 False
2.0 67.0 -1.3858 0.1992 1.0 False
2.0 68.0 0.1268 0.9019 1.0 False
2.0 69.0 0.1268 0.9019 1.0 False
2.0 70.0 -3.1707 0.0114 1.0 False
2.0 71.0 -1.6815 0.127 1.0 False
2.0 72.0 -2.5762 0.0299 1.0 False
2.0 73.0 -1.7327 0.1172 1.0 False
2.0 74.0 -1.2837 0.2313 1.0 False
2.0 75.0 -1.5607 0.153 1.0 False
3.0 4.0 -1.4441 0.1826 1.0 False
3.0 5.0 -1.4441 0.1826 1.0 False
3.0 6.0 -0.4473 0.6652 1.0 False
3.0 7.0 1.0136 0.3372 1.0 False
3.0 8.0 0.8516 0.4165 1.0 False
3.0 9.0 -0.9026 0.3903 1.0 False
3.0 10.0 1.0819 0.3074 1.0 False
3.0 11.0 1.0819 0.3074 1.0 False
3.0 12.0 -1.1123 0.2948 1.0 False
3.0 13.0 -0.5404 0.6021 1.0 False
3.0 14.0 -0.3241 0.7532 1.0 False
3.0 15.0 -0.7076 0.4971 1.0 False
3.0 16.0 -0.3403 0.7414 1.0 False
3.0 17.0 0.817 0.435 1.0 False
3.0 18.0 0.8533 0.4156 1.0 False
3.0 19.0 1.04 0.3255 1.0 False
3.0 20.0 -0.1474 0.8861 1.0 False
3.0 21.0 0.5258 0.6117 1.0 False
3.0 22.0 -0.876 0.4038 1.0 False
3.0 23.0 -0.282 0.7843 1.0 False
3.0 24.0 0.1717 0.8675 1.0 False
3.0 25.0 0.5605 0.5888 1.0 False
3.0 26.0 -0.3458 0.7375 1.0 False
3.0 27.0 -0.3458 0.7375 1.0 False
3.0 28.0 0.512 0.621 1.0 False
3.0 29.0 -0.1389 0.8926 1.0 False
3.0 30.0 -0.5199 0.6157 1.0 False
3.0 31.0 0.1025 0.9206 1.0 False
3.0 32.0 -0.4634 0.6541 1.0 False
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56.0 64.0 -2.254 0.0507 1.0 False
56.0 65.0 -2.1726 0.0579 1.0 False
56.0 66.0 -2.0753 0.0678 1.0 False
56.0 67.0 -2.0051 0.0759 1.0 False
56.0 68.0 -0.6249 0.5476 1.0 False
56.0 69.0 -0.6249 0.5476 1.0 False
56.0 70.0 -3.3913 0.008 1.0 False
56.0 71.0 -2.254 0.0507 1.0 False
56.0 72.0 -2.7505 0.0225 1.0 False
56.0 73.0 -2.0971 0.0654 1.0 False
56.0 74.0 -2.0753 0.0678 1.0 False
56.0 75.0 -2.0846 0.0668 1.0 False
57.0 58.0 -6.3222 0.0001 0.3914 False
57.0 59.0 -2.5617 0.0306 1.0 False
57.0 60.0 0.3393 0.7422 1.0 False
57.0 61.0 0.3393 0.7422 1.0 False
57.0 62.0 -2.279 0.0486 1.0 False
57.0 63.0 -2.9106 0.0173 1.0 False
57.0 64.0 -2.254 0.0507 1.0 False
57.0 65.0 -2.1726 0.0579 1.0 False
57.0 66.0 -2.0753 0.0678 1.0 False
57.0 67.0 -2.0051 0.0759 1.0 False
57.0 68.0 -0.6249 0.5476 1.0 False
57.0 69.0 -0.6249 0.5476 1.0 False
57.0 70.0 -3.3913 0.008 1.0 False
57.0 71.0 -2.254 0.0507 1.0 False
57.0 72.0 -2.7505 0.0225 1.0 False
57.0 73.0 -2.0971 0.0654 1.0 False
57.0 74.0 -2.0753 0.0678 1.0 False
57.0 75.0 -2.0846 0.0668 1.0 False
58.0 59.0 3.8666 0.0038 1.0 False
58.0 60.0 7.4761 0.0 0.1079 False
58.0 61.0 7.4761 0.0 0.1079 False
58.0 62.0 2.0902 0.0662 1.0 False
58.0 63.0 2.2971 0.0472 1.0 False
58.0 64.0 2.3716 0.0418 1.0 False
58.0 65.0 1.7261 0.1184 1.0 False
58.0 66.0 1.9099 0.0885 1.0 False
58.0 67.0 2.6889 0.0248 1.0 False
58.0 68.0 3.3333 0.0088 1.0 False
58.0 69.0 3.3333 0.0088 1.0 False
58.0 70.0 2.076 0.0677 1.0 False
58.0 71.0 2.3716 0.0418 1.0 False
58.0 72.0 1.5347 0.1592 1.0 False
58.0 73.0 2.4935 0.0342 1.0 False
58.0 74.0 1.9099 0.0885 1.0 False
58.0 75.0 1.8974 0.0903 1.0 False
59.0 60.0 3.9947 0.0031 1.0 False
59.0 61.0 3.9947 0.0031 1.0 False
59.0 62.0 0.0289 0.9776 1.0 False
59.0 63.0 -0.5848 0.5731 1.0 False
59.0 64.0 0.0721 0.9441 1.0 False
59.0 65.0 -0.3001 0.7709 1.0 False
59.0 66.0 0.0852 0.9339 1.0 False
59.0 67.0 0.41 0.6914 1.0 False
59.0 68.0 1.3019 0.2253 1.0 False
59.0 69.0 1.3019 0.2253 1.0 False
59.0 70.0 -1.0472 0.3223 1.0 False
59.0 71.0 0.0721 0.9441 1.0 False
59.0 72.0 -0.8293 0.4284 1.0 False
59.0 73.0 0.097 0.9249 1.0 False
59.0 74.0 0.0852 0.9339 1.0 False
59.0 75.0 0.0014 0.999 1.0 False
60.0 61.0 nan nan nan False
60.0 62.0 -2.5577 0.0308 1.0 False
60.0 63.0 -3.6671 0.0052 1.0 False
60.0 64.0 -2.8444 0.0193 1.0 False
60.0 65.0 -3.7909 0.0043 1.0 False
60.0 66.0 -2.2442 0.0515 1.0 False
60.0 67.0 -2.3254 0.0451 1.0 False
60.0 68.0 -0.9117 0.3857 1.0 False
60.0 69.0 -0.9117 0.3857 1.0 False
60.0 70.0 -4.2402 0.0022 1.0 False
60.0 71.0 -2.8444 0.0193 1.0 False
60.0 72.0 -4.5222 0.0014 1.0 False
60.0 73.0 -3.5785 0.0059 1.0 False
60.0 74.0 -2.2442 0.0515 1.0 False
60.0 75.0 -2.6427 0.0268 1.0 False
61.0 62.0 -2.5577 0.0308 1.0 False
61.0 63.0 -3.6671 0.0052 1.0 False
61.0 64.0 -2.8444 0.0193 1.0 False
61.0 65.0 -3.7909 0.0043 1.0 False
61.0 66.0 -2.2442 0.0515 1.0 False
61.0 67.0 -2.3254 0.0451 1.0 False
61.0 68.0 -0.9117 0.3857 1.0 False
61.0 69.0 -0.9117 0.3857 1.0 False
61.0 70.0 -4.2402 0.0022 1.0 False
61.0 71.0 -2.8444 0.0193 1.0 False
61.0 72.0 -4.5222 0.0014 1.0 False
61.0 73.0 -3.5785 0.0059 1.0 False
61.0 74.0 -2.2442 0.0515 1.0 False
61.0 75.0 -2.6427 0.0268 1.0 False
62.0 63.0 -0.8232 0.4316 1.0 False
62.0 64.0 0.116 0.9102 1.0 False
62.0 65.0 -0.6116 0.5559 1.0 False
62.0 66.0 0.1381 0.8932 1.0 False
62.0 67.0 1.4989 0.1681 1.0 False
62.0 68.0 3.4214 0.0076 1.0 False
62.0 69.0 3.4214 0.0076 1.0 False
62.0 70.0 -0.9259 0.3787 1.0 False
62.0 71.0 0.116 0.9102 1.0 False
62.0 72.0 -1.3539 0.2088 1.0 False
62.0 73.0 0.1029 0.9203 1.0 False
62.0 74.0 0.1381 0.8932 1.0 False
62.0 75.0 -0.0916 0.929 1.0 False
63.0 64.0 1.0157 0.3363 1.0 False
63.0 65.0 0.2118 0.837 1.0 False
63.0 66.0 0.9475 0.3681 1.0 False
63.0 67.0 1.8667 0.0948 1.0 False
63.0 68.0 2.7174 0.0237 1.0 False
63.0 69.0 2.7174 0.0237 1.0 False
63.0 70.0 -0.6976 0.5031 1.0 False
63.0 71.0 1.0157 0.3363 1.0 False
63.0 72.0 -0.5156 0.6186 1.0 False
63.0 73.0 0.9365 0.3735 1.0 False
63.0 74.0 0.9475 0.3681 1.0 False
63.0 75.0 0.7429 0.4765 1.0 False
64.0 65.0 -0.752 0.4713 1.0 False
64.0 66.0 0.0533 0.9587 1.0 False
64.0 67.0 0.9652 0.3597 1.0 False
64.0 68.0 2.8506 0.0191 1.0 False
64.0 69.0 2.8506 0.0191 1.0 False
64.0 70.0 -0.9924 0.3469 1.0 False
64.0 71.0 nan nan nan False
64.0 72.0 -1.5922 0.1458 1.0 False
64.0 73.0 0.0516 0.96 1.0 False
64.0 74.0 0.0533 0.9587 1.0 False
64.0 75.0 -0.225 0.827 1.0 False
65.0 66.0 0.5363 0.6048 1.0 False
65.0 67.0 1.2059 0.2586 1.0 False
65.0 68.0 3.0251 0.0144 1.0 False
65.0 69.0 3.0251 0.0144 1.0 False
65.0 70.0 -0.475 0.6461 1.0 False
65.0 71.0 0.752 0.4713 1.0 False
65.0 72.0 -1.5 0.1679 1.0 False
65.0 73.0 0.9226 0.3803 1.0 False
65.0 74.0 0.5363 0.6048 1.0 False
65.0 75.0 0.6301 0.5443 1.0 False
66.0 67.0 0.4988 0.6299 1.0 False
66.0 68.0 2.5634 0.0305 1.0 False
66.0 69.0 2.5634 0.0305 1.0 False
66.0 70.0 -1.1723 0.2712 1.0 False
66.0 71.0 -0.0533 0.9587 1.0 False
66.0 72.0 -1.0646 0.3148 1.0 False
66.0 73.0 -0.019 0.9852 1.0 False
66.0 74.0 nan nan nan False
66.0 75.0 -0.2032 0.8435 1.0 False
67.0 68.0 2.0632 0.0691 1.0 False
67.0 69.0 2.0632 0.0691 1.0 False
67.0 70.0 -1.6045 0.1431 1.0 False
67.0 71.0 -0.9652 0.3597 1.0 False
67.0 72.0 -2.0276 0.0732 1.0 False
67.0 73.0 -0.5988 0.5641 1.0 False
67.0 74.0 -0.4988 0.6299 1.0 False
67.0 75.0 -1.0146 0.3368 1.0 False
68.0 69.0 nan nan nan False
68.0 70.0 -2.4253 0.0383 1.0 False
68.0 71.0 -2.8506 0.0191 1.0 False
68.0 72.0 -3.261 0.0098 1.0 False
68.0 73.0 -2.2975 0.0472 1.0 False
68.0 74.0 -2.5634 0.0305 1.0 False
68.0 75.0 -2.7788 0.0214 1.0 False
69.0 70.0 -2.4253 0.0383 1.0 False
69.0 71.0 -2.8506 0.0191 1.0 False
69.0 72.0 -3.261 0.0098 1.0 False
69.0 73.0 -2.2975 0.0472 1.0 False
69.0 74.0 -2.5634 0.0305 1.0 False
69.0 75.0 -2.7788 0.0214 1.0 False
70.0 71.0 0.9924 0.3469 1.0 False
70.0 72.0 -0.0264 0.9795 1.0 False
70.0 73.0 1.0123 0.3378 1.0 False
70.0 74.0 1.1723 0.2712 1.0 False
70.0 75.0 0.8812 0.4011 1.0 False
71.0 72.0 -1.5922 0.1458 1.0 False
71.0 73.0 0.0516 0.96 1.0 False
71.0 74.0 0.0533 0.9587 1.0 False
71.0 75.0 -0.225 0.827 1.0 False
72.0 73.0 1.5827 0.1479 1.0 False
72.0 74.0 1.0646 0.3148 1.0 False
72.0 75.0 1.3714 0.2035 1.0 False
73.0 74.0 0.019 0.9852 1.0 False
73.0 75.0 -0.1818 0.8598 1.0 False
74.0 75.0 -0.2032 0.8435 1.0 False
----------------------------------------------
In [335]:
thr = 10
In [336]:
dfDesc = df.sort_values("cv_f1_mean", ascending=False)
dfDesc[["fs","sm","metric","cls","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
'test_prec', 'test_rec',"test_f1"]].iloc[:thr,:]
Out[336]:
fs
sm
metric
cls
params
tr_f1
cv_f1
cv_prec
cv_rec
test_prec
test_rec
test_f1
50
none
none
f1_weighted
svmRBF
[0.001, None, 50]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.77
0.85
0.76
48
none
none
f1_weighted
nn
[(30, 30), 0.01]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.77
0.85
0.76
43
none
none
precision_weighted
svmRBF
[0.001, None, 50]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.77
0.85
0.76
44
none
none
f1_weighted
logReg
[None, 30, l2]
1.00
0.7+/-0.16
0.75+/-0.17
0.71+/-0.16
0.77
0.85
0.76
45
none
none
precision_weighted
logReg
[None, 30, l2]
1.00
0.7+/-0.16
0.75+/-0.17
0.71+/-0.16
0.77
0.85
0.76
53
none
after
precision_weighted
svmRBF
[0.001, None, 3, 50]
1.00
0.7+/-0.16
0.75+/-0.19
0.71+/-0.15
0.77
0.85
0.76
52
none
after
f1_weighted
svmRBF
[0.001, None, 3, 50]
1.00
0.7+/-0.16
0.75+/-0.19
0.71+/-0.15
0.77
0.85
0.76
58
none
after
f1_weighted
nn
[(50, 50), 4, 0.01]
1.00
0.7+/-0.14
0.77+/-0.15
0.71+/-0.13
0.77
0.85
0.76
49
none
none
precision_weighted
nn
[(150,), 0.01]
1.00
0.69+/-0.18
0.76+/-0.2
0.71+/-0.17
0.77
0.85
0.76
12
combine_fs
after
f1_weighted
svmRBF
[0.001, None, 3, 40, 15]
0.93
0.68+/-0.19
0.7+/-0.2
0.69+/-0.18
0.62
0.79
0.57
In [337]:
plt.figure(figsize=(10,6))
plt.errorbar(x = range(thr), y= df["cv_f1_mean"].iloc[:thr], yerr=df["cv_f1_std"].iloc[:thr], linestyle='None', marker='o')
plt.xticks(range(thr),df["cls"].iloc[:thr].str.cat(df["params"].iloc[:thr].astype(str), sep="").
str.cat(df["fs"].iloc[:thr],sep="_").
str.cat(df["sm"].iloc[:thr],sep="_").
str.cat(df["metric"].iloc[:thr],sep="_"),
rotation=90)
plt.ylabel("avg (CV f1_score)")
plt.ylim(0.4,1.05)
plt.xlim(-1,11)
plt.axhline(0.5,c="r",linestyle="--", label="random")
plt.show()
In [371]:
thr=df.shape[0] #10
In [372]:
dfOrd = df.sort_values("cv_f1_mean", ascending=False)
#Classifier algorithms
dfCls = dfOrd.iloc[:thr,:].groupby(["cls"]).count().reset_index()
dfCls["cont"] = dfCls.iloc[:,-1]
dfCls = dfCls[["cls","cont"]]
dfClsPerf = dfOrd.iloc[:thr,:][["cls","cv_f1_mean"]].groupby(["cls"]).agg(["mean","max","min","std"]).reset_index()
dfClsPerf["cv_mean"] = dfClsPerf.iloc[:,1]
dfClsPerf["cv_max"] = dfClsPerf.iloc[:,2]
dfClsPerf["cv_min"] = dfClsPerf.iloc[:,3]
dfClsPerf["cv_std"] = dfClsPerf.iloc[:,4]
dfClsPerf = dfClsPerf[["cls","cv_mean","cv_max","cv_min","cv_std"]]
dfClsMerged = pd.merge(dfCls,dfClsPerf,on="cls")
dfClsMerged["cv_mean"] = dfClsMerged.iloc[:,2]
dfClsMerged["cv_max"] = dfClsMerged.iloc[:,3]
dfClsMerged["cv_min"] = dfClsMerged.iloc[:,4]
dfClsMerged["cv_std"] = dfClsMerged.iloc[:,5]
dfClsMerged = dfClsMerged[["cls","cont","cv_mean","cv_max","cv_min","cv_std"]].sort_values(["cont","cv_mean"], ascending=False)
display(dfClsMerged)
#Feature selection
dfFs = dfOrd.iloc[:thr,:].groupby(["fs"]).count().reset_index()
dfFs["cont"] = dfFs.iloc[:,-1]
dfFs = dfFs[["fs","cont"]]
dfFsPerf = dfOrd.iloc[:thr,:][["fs","cv_f1_mean"]].groupby(["fs"]).agg(["mean","max","min","std"]).reset_index()
dfFsPerf["cv_mean"] = dfFsPerf.iloc[:,1]
dfFsPerf["cv_max"] = dfFsPerf.iloc[:,2]
dfFsPerf["cv_min"] = dfFsPerf.iloc[:,3]
dfFsPerf["cv_std"] = dfFsPerf.iloc[:,4]
dfFsPerf = dfFsPerf[["fs","cv_mean","cv_max","cv_min","cv_std"]]
dfFsMerged = pd.merge(dfFs,dfFsPerf,on="fs")
dfFsMerged["cv_mean"] = dfFsMerged.iloc[:,2]
dfFsMerged["cv_max"] = dfFsMerged.iloc[:,3]
dfFsMerged["cv_min"] = dfFsMerged.iloc[:,4]
dfFsMerged["cv_std"] = dfFsMerged.iloc[:,5]
dfFsMerged = dfFsMerged[["fs","cont","cv_mean","cv_max","cv_min","cv_std"]].sort_values(["cont","cv_mean"], ascending=False)
display(dfFsMerged)
#Sampling
dfSm = dfOrd.iloc[:thr,:].groupby(["sm"]).count().reset_index()
dfSm["cont"] = dfSm.iloc[:,-1]
dfSm = dfSm[["sm","cont"]]
dfSmPerf = dfOrd.iloc[:thr,:][["sm","cv_f1_mean"]].groupby(["sm"]).agg(["mean","max","min","std"]).reset_index()
dfSmPerf["cv_mean"] = dfSmPerf.iloc[:,1]
dfSmPerf["cv_max"] = dfSmPerf.iloc[:,2]
dfSmPerf["cv_min"] = dfSmPerf.iloc[:,3]
dfSmPerf["cv_std"] = dfSmPerf.iloc[:,4]
dfSmPerf = dfSmPerf[["sm","cv_mean","cv_max","cv_min","cv_std"]]
dfSmMerged = pd.merge(dfSm,dfSmPerf,on="sm")
dfSmMerged["cv_mean"] = dfSmMerged.iloc[:,2]
dfSmMerged["cv_max"] = dfSmMerged.iloc[:,3]
dfSmMerged["cv_min"] = dfSmMerged.iloc[:,4]
dfSmMerged["cv_std"] = dfSmMerged.iloc[:,5]
dfSmMerged = dfSmMerged[["sm","cont","cv_mean","cv_max","cv_min","cv_std"]].sort_values(["cont","cv_mean"], ascending=False)
display(dfSmMerged)
#Metric
dfMetric = dfOrd.iloc[:thr,:].groupby(["metric"]).count().reset_index()
dfMetric["cont"] = dfMetric.iloc[:,-1]
dfMetric = dfMetric[["metric","cont"]]
dfMetricPerf = dfOrd.iloc[:thr,:][["metric","cv_f1_mean"]].groupby(["metric"]).agg(["mean","max","min","std"]).reset_index()
dfMetricPerf["cv_mean"] = dfMetricPerf.iloc[:,1]
dfMetricPerf["cv_max"] = dfMetricPerf.iloc[:,2]
dfMetricPerf["cv_min"] = dfMetricPerf.iloc[:,3]
dfMetricPerf["cv_std"] = dfMetricPerf.iloc[:,4]
dfMetricPerf = dfMetricPerf[["metric","cv_mean","cv_max","cv_min","cv_std"]]
dfMetricMerged = pd.merge(dfMetric,dfMetricPerf,on="metric")
dfMetricMerged["cv_mean"] = dfMetricMerged.iloc[:,2]
dfMetricMerged["cv_max"] = dfMetricMerged.iloc[:,3]
dfMetricMerged["cv_min"] = dfMetricMerged.iloc[:,4]
dfMetricMerged["cv_std"] = dfMetricMerged.iloc[:,5]
dfMetricMerged = dfMetricMerged[["metric","cont","cv_mean","cv_max","cv_min","cv_std"]].sort_values(["cont","cv_mean"], ascending=False)
display(dfMetricMerged)
cls
cont
cv_mean
cv_max
cv_min
cv_std
2
nn
16
0.60
0.71
0.49
0.06
4
svmRBF
16
0.59
0.71
0.45
0.10
1
logReg
16
0.59
0.70
0.49
0.07
0
knn
16
0.58
0.66
0.49
0.04
3
rf
12
0.57
0.61
0.53
0.03
fs
cont
cv_mean
cv_max
cv_min
cv_std
2
none
20
0.65
0.71
0.57
0.06
0
combine_fs
20
0.56
0.68
0.47
0.06
1
lasso_fs
20
0.53
0.60
0.45
0.04
3
rfe_rf_fs
16
0.61
0.66
0.49
0.04
sm
cont
cv_mean
cv_max
cv_min
cv_std
1
none
38
0.59
0.71
0.47
0.07
0
after
38
0.58
0.70
0.45
0.07
metric
cont
cv_mean
cv_max
cv_min
cv_std
0
f1_weighted
38
0.59
0.71
0.45
0.07
1
precision_weighted
38
0.59
0.71
0.45
0.07
In [373]:
dfDesc = df[np.logical_not(df["cls"].isin(["svmRBF","nn"]))]\
.sort_values("cv_f1_mean", ascending=False)
dfDesc[["fs","sm","metric","cls","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
"test_f1",'test_prec', 'test_rec']].iloc[:thr,:]
#"ds","exp",
Out[373]:
fs
sm
metric
cls
params
tr_f1
cv_f1
cv_prec
cv_rec
test_f1
test_prec
test_rec
44
none
none
f1_weighted
logReg
[None, 30, l2]
1.00
0.7+/-0.16
0.75+/-0.17
0.71+/-0.16
0.76
0.77
0.85
45
none
none
precision_weighted
logReg
[None, 30, l2]
1.00
0.7+/-0.16
0.75+/-0.17
0.71+/-0.16
0.76
0.77
0.85
69
rfe_rf_fs
none
f1_weighted
knn
[21, uniform, 5, 0.1]
0.86
0.66+/-0.2
0.7+/-0.21
0.68+/-0.19
0.60
0.62
0.66
55
none
after
precision_weighted
logReg
[None, 5, 4, l2]
1.00
0.66+/-0.2
0.68+/-0.23
0.68+/-0.17
0.76
0.77
0.85
54
none
after
f1_weighted
logReg
[None, 5, 4, l2]
1.00
0.66+/-0.2
0.68+/-0.23
0.68+/-0.17
0.76
0.77
0.85
68
rfe_rf_fs
none
precision_weighted
knn
[32, uniform, 5, 0.1]
0.90
0.64+/-0.21
0.66+/-0.24
0.68+/-0.18
0.69
0.69
0.72
70
rfe_rf_fs
none
f1_weighted
logReg
[43, balanced, 5, 0.1, l2]
1.00
0.62+/-0.19
0.64+/-0.23
0.64+/-0.16
0.44
0.54
0.77
67
rfe_rf_fs
none
precision_weighted
logReg
[43, balanced, 5, 0.1, l2]
1.00
0.62+/-0.19
0.64+/-0.23
0.64+/-0.16
0.44
0.54
0.77
7
combine_fs
none
precision_weighted
knn
[uniform, 3, 20]
0.93
0.62+/-0.14
0.62+/-0.16
0.63+/-0.12
0.60
0.62
0.66
6
combine_fs
none
f1_weighted
knn
[uniform, 3, 20]
0.93
0.62+/-0.14
0.62+/-0.16
0.63+/-0.12
0.60
0.62
0.66
59
none
after
f1_weighted
rf
[gini, None, None, 150, 5]
1.00
0.61+/-0.18
0.66+/-0.2
0.63+/-0.18
0.84
0.85
0.88
51
none
after
precision_weighted
rf
[gini, None, None, 150, 5]
1.00
0.61+/-0.18
0.66+/-0.2
0.63+/-0.18
0.84
0.85
0.88
63
rfe_rf_fs
after
f1_weighted
logReg
[0.1, 4, l2, None, 5, 43]
1.00
0.61+/-0.18
0.62+/-0.21
0.63+/-0.16
0.44
0.54
0.77
62
rfe_rf_fs
after
precision_weighted
logReg
[0.1, 4, l2, None, 5, 43]
1.00
0.61+/-0.18
0.62+/-0.21
0.63+/-0.16
0.44
0.54
0.77
40
none
none
f1_weighted
rf
[entropy, 4, None, 100]
1.00
0.6+/-0.13
0.68+/-0.16
0.63+/-0.13
0.68
0.69
0.71
42
none
none
precision_weighted
rf
[entropy, 4, None, 100]
1.00
0.6+/-0.13
0.68+/-0.16
0.63+/-0.13
0.68
0.69
0.71
34
lasso_fs
none
precision_weighted
knn
[1, uniform, 5]
0.97
0.6+/-0.16
0.65+/-0.18
0.62+/-0.14
0.84
0.85
0.88
35
lasso_fs
none
f1_weighted
knn
[1, uniform, 5]
0.97
0.6+/-0.16
0.65+/-0.18
0.62+/-0.14
0.84
0.85
0.88
14
combine_fs
after
f1_weighted
logReg
[None, 5, l2, 50, 3]
1.00
0.59+/-0.22
0.6+/-0.24
0.61+/-0.2
0.57
0.62
0.79
26
lasso_fs
after
precision_weighted
logReg
[None, 1, 1e-05, 5, l2]
0.97
0.59+/-0.22
0.6+/-0.24
0.61+/-0.19
0.57
0.62
0.79
61
rfe_rf_fs
after
f1_weighted
knn
[43, 11, uniform, 0.1, 5]
0.76
0.58+/-0.16
0.65+/-0.18
0.6+/-0.15
0.44
0.54
0.77
36
lasso_fs
none
precision_weighted
logReg
[balanced, 1, 1e-05, l2]
0.97
0.58+/-0.22
0.6+/-0.25
0.6+/-0.21
0.57
0.62
0.79
16
combine_fs
after
f1_weighted
knn
[uniform, 3, 20, 3]
0.90
0.57+/-0.16
0.59+/-0.18
0.59+/-0.14
0.67
0.69
0.82
56
none
after
f1_weighted
knn
[uniform, 11, 5]
0.68
0.57+/-0.17
0.62+/-0.21
0.6+/-0.14
0.67
0.69
0.82
57
none
after
precision_weighted
knn
[uniform, 11, 5]
0.68
0.57+/-0.17
0.62+/-0.21
0.6+/-0.14
0.67
0.69
0.82
47
none
none
precision_weighted
knn
[uniform, 3]
0.79
0.57+/-0.15
0.59+/-0.19
0.59+/-0.14
0.46
0.46
0.47
46
none
none
f1_weighted
knn
[uniform, 3]
0.79
0.57+/-0.15
0.59+/-0.19
0.59+/-0.14
0.46
0.46
0.47
25
lasso_fs
after
f1_weighted
knn
[1, uniform, 7, 4]
0.86
0.56+/-0.22
0.62+/-0.26
0.59+/-0.2
0.57
0.62
0.79
11
combine_fs
after
precision_weighted
rf
[4, 100, gini, 3, None, 20]
0.97
0.56+/-0.16
0.56+/-0.18
0.59+/-0.13
0.53
0.54
0.55
10
combine_fs
after
f1_weighted
rf
[4, 100, gini, 3, None, 20]
0.97
0.56+/-0.16
0.56+/-0.18
0.59+/-0.13
0.53
0.54
0.55
8
combine_fs
none
f1_weighted
rf
[150, entropy, 4, balanced, 20]
1.00
0.55+/-0.14
0.58+/-0.2
0.59+/-0.12
0.69
0.69
0.72
1
combine_fs
none
precision_weighted
rf
[150, entropy, 4, balanced, 20]
1.00
0.55+/-0.14
0.58+/-0.2
0.59+/-0.12
0.69
0.69
0.72
39
lasso_fs
none
precision_weighted
rf
[entropy, 1, 4, balanced, 200]
1.00
0.55+/-0.19
0.59+/-0.23
0.58+/-0.18
0.61
0.62
0.61
21
lasso_fs
none
f1_weighted
rf
[entropy, 1, 4, balanced, 200]
1.00
0.55+/-0.19
0.59+/-0.23
0.58+/-0.18
0.61
0.62
0.61
24
lasso_fs
after
precision_weighted
knn
[1, distance, 11, 4]
1.00
0.55+/-0.22
0.63+/-0.24
0.57+/-0.22
0.57
0.62
0.79
31
lasso_fs
after
f1_weighted
rf
[1, 100, entropy, 5, 4, None]
1.00
0.54+/-0.18
0.56+/-0.19
0.56+/-0.18
0.54
0.54
0.54
30
lasso_fs
after
precision_weighted
rf
[1, 100, gini, 3, None, None]
1.00
0.53+/-0.21
0.55+/-0.22
0.54+/-0.21
0.69
0.69
0.72
15
combine_fs
after
precision_weighted
logReg
[None, 15, l1, 30, 3]
1.00
0.52+/-0.2
0.55+/-0.21
0.53+/-0.2
0.57
0.62
0.79
0
combine_fs
after
precision_weighted
knn
[uniform, 7, 10, 4]
0.90
0.52+/-0.19
0.54+/-0.22
0.54+/-0.18
0.44
0.46
0.47
5
combine_fs
none
precision_weighted
logReg
[None, 30, l1, 30]
1.00
0.5+/-0.19
0.52+/-0.19
0.51+/-0.19
0.57
0.62
0.79
4
combine_fs
none
f1_weighted
logReg
[None, 30, l1, 30]
1.00
0.5+/-0.19
0.52+/-0.19
0.51+/-0.19
0.57
0.62
0.79
37
lasso_fs
none
f1_weighted
logReg
[None, 1, 0.5, l1]
0.93
0.5+/-0.2
0.5+/-0.21
0.51+/-0.18
0.76
0.77
0.85
60
rfe_rf_fs
after
precision_weighted
knn
[43, 7, uniform, 0.1, 5]
0.68
0.49+/-0.19
0.56+/-0.23
0.52+/-0.17
0.44
0.54
0.77
27
lasso_fs
after
f1_weighted
logReg
[None, 1, 0.5, 5, l1]
0.93
0.49+/-0.14
0.49+/-0.16
0.5+/-0.12
0.76
0.77
0.85
In [374]:
dfDesc.boxplot(by="cls",column=["cv_f1_mean"])
plt.ylim(0.4,1.0)
plt.suptitle("")
plt.xlabel("")
plt.title("")
Out[374]:
<matplotlib.text.Text at 0x7f874d69c750>
In [375]:
#Pick one randomly (from the group with same f1)
idx = dfDesc.groupby(['cls'])['cv_f1_mean'].transform(max) == dfDesc['cv_f1_mean']
dfDescIdx = dfDesc[idx]
dfDescIdx = dfDescIdx.groupby("cls").first()
print dfDescIdx.exp.values
dfDescIdx[["fs","sm","metric","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
"test_f1",'test_prec', 'test_rec']]
[69 44 59]
Out[375]:
fs
sm
metric
params
tr_f1
cv_f1
cv_prec
cv_rec
test_f1
test_prec
test_rec
cls
knn
rfe_rf_fs
none
f1_weighted
[21, uniform, 5, 0.1]
0.86
0.66+/-0.2
0.7+/-0.21
0.68+/-0.19
0.60
0.62
0.66
logReg
none
none
f1_weighted
[None, 30, l2]
1.00
0.7+/-0.16
0.75+/-0.17
0.71+/-0.16
0.76
0.77
0.85
rf
none
after
f1_weighted
[gini, None, None, 150, 5]
1.00
0.61+/-0.18
0.66+/-0.2
0.63+/-0.18
0.84
0.85
0.88
In [376]:
st, p = stats.f_oneway(*[cvs[cvs[:,0] == i,1] for i in dfDescIdx.exp.values])
print "stat:",st, "p-value:", p
print "Means:",[np.mean(cvs[cvs[:,0] == i,1]) for i in dfDescIdx.exp.values]
print "std:",[np.std(cvs[cvs[:,0] == i,1]) for i in dfDescIdx.exp.values]
if p < 0.05:
#Find pair wise significance tests
mc = MultiComparison(cvs[:,1], cvs[:,0])
result = mc.tukeyhsd()
print(result)
print(mc.groupsunique)
stat: nan p-value: nan
Means: [nan, nan, nan]
std: [nan, nan, nan]
In [377]:
dfDescIdx[["cv_f1_mean","test_f1"]].plot(kind="bar", yerr=[[0.07,0.13,0.15],[0.0,0.0,0.0]], rot=0,alpha=0.5)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.xlabel("")
plt.title("")
Out[377]:
<matplotlib.text.Text at 0x7f874c87fcd0>
In [378]:
idx = dfDesc.groupby(['cls'])['cv_f1_mean'].transform(max) == dfDesc['cv_f1_mean']
idx2 = dfDesc[idx].groupby(['cv_f1_mean'])['num_nones'].transform(max) == dfDesc[idx]['num_nones']
dfDesc[idx][idx2][["num_nones","fs","sm","metric","cls","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
"test_f1",'test_prec', 'test_rec']]
Out[378]:
num_nones
fs
sm
metric
cls
params
tr_f1
cv_f1
cv_prec
cv_rec
test_f1
test_prec
test_rec
44
2
none
none
f1_weighted
logReg
[None, 30, l2]
1.00
0.7+/-0.16
0.75+/-0.17
0.71+/-0.16
0.76
0.77
0.85
45
2
none
none
precision_weighted
logReg
[None, 30, l2]
1.00
0.7+/-0.16
0.75+/-0.17
0.71+/-0.16
0.76
0.77
0.85
69
1
rfe_rf_fs
none
f1_weighted
knn
[21, uniform, 5, 0.1]
0.86
0.66+/-0.2
0.7+/-0.21
0.68+/-0.19
0.60
0.62
0.66
59
1
none
after
f1_weighted
rf
[gini, None, None, 150, 5]
1.00
0.61+/-0.18
0.66+/-0.2
0.63+/-0.18
0.84
0.85
0.88
51
1
none
after
precision_weighted
rf
[gini, None, None, 150, 5]
1.00
0.61+/-0.18
0.66+/-0.2
0.63+/-0.18
0.84
0.85
0.88
In [379]:
dfDesc = df[df["cls"].isin(["svmRBF","nn"])]\
.sort_values("cv_f1_mean", ascending=False)
dfDesc[["fs","sm","metric","cls","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
"test_f1",'test_prec', 'test_rec']].iloc[:thr,:]
Out[379]:
fs
sm
metric
cls
params
tr_f1
cv_f1
cv_prec
cv_rec
test_f1
test_prec
test_rec
50
none
none
f1_weighted
svmRBF
[0.001, None, 50]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
43
none
none
precision_weighted
svmRBF
[0.001, None, 50]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
48
none
none
f1_weighted
nn
[(30, 30), 0.01]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
53
none
after
precision_weighted
svmRBF
[0.001, None, 3, 50]
1.00
0.7+/-0.16
0.75+/-0.19
0.71+/-0.15
0.76
0.77
0.85
52
none
after
f1_weighted
svmRBF
[0.001, None, 3, 50]
1.00
0.7+/-0.16
0.75+/-0.19
0.71+/-0.15
0.76
0.77
0.85
58
none
after
f1_weighted
nn
[(50, 50), 4, 0.01]
1.00
0.7+/-0.14
0.77+/-0.15
0.71+/-0.13
0.76
0.77
0.85
49
none
none
precision_weighted
nn
[(150,), 0.01]
1.00
0.69+/-0.18
0.76+/-0.2
0.71+/-0.17
0.76
0.77
0.85
12
combine_fs
after
f1_weighted
svmRBF
[0.001, None, 3, 40, 15]
0.93
0.68+/-0.19
0.7+/-0.2
0.69+/-0.18
0.57
0.62
0.79
41
none
after
precision_weighted
nn
[(70, 70, 70), 4, 0.01]
1.00
0.67+/-0.18
0.71+/-0.22
0.7+/-0.15
0.76
0.77
0.85
13
combine_fs
after
precision_weighted
svmRBF
[0.01, None, 3, 20, 1]
0.97
0.66+/-0.15
0.71+/-0.18
0.69+/-0.13
0.76
0.77
0.85
65
rfe_rf_fs
after
f1_weighted
svmRBF
[0.1, None, 4, 30, 43, 0.001]
1.00
0.62+/-0.23
0.63+/-0.26
0.64+/-0.2
0.44
0.54
0.77
64
rfe_rf_fs
after
precision_weighted
svmRBF
[0.1, None, 4, 30, 43, 0.001]
1.00
0.62+/-0.23
0.63+/-0.26
0.64+/-0.2
0.44
0.54
0.77
73
rfe_rf_fs
after
f1_weighted
nn
[43, (70, 70, 70), 0.1, 5, 0.001]
1.00
0.61+/-0.18
0.63+/-0.22
0.63+/-0.16
0.44
0.54
0.77
71
rfe_rf_fs
none
precision_weighted
svmRBF
[43, 0.001, balanced, 0.1, 50]
1.00
0.61+/-0.22
0.62+/-0.25
0.63+/-0.19
0.44
0.54
0.77
72
rfe_rf_fs
none
f1_weighted
svmRBF
[43, 0.001, balanced, 0.1, 50]
1.00
0.61+/-0.22
0.62+/-0.25
0.63+/-0.19
0.44
0.54
0.77
75
rfe_rf_fs
after
precision_weighted
nn
[43, (70, 70), 0.1, 3, 0.01]
1.00
0.61+/-0.21
0.63+/-0.25
0.63+/-0.19
0.44
0.54
0.77
74
rfe_rf_fs
none
f1_weighted
nn
[43, (70, 70, 70), 0.1, 0.01]
1.00
0.6+/-0.21
0.61+/-0.24
0.62+/-0.18
0.44
0.54
0.77
66
rfe_rf_fs
none
precision_weighted
nn
[43, (70, 70, 70), 0.1, 0.01]
1.00
0.6+/-0.21
0.61+/-0.24
0.62+/-0.18
0.44
0.54
0.77
17
combine_fs
none
precision_weighted
nn
[(150,), 50, 0.001]
1.00
0.59+/-0.21
0.6+/-0.24
0.62+/-0.18
0.67
0.69
0.82
18
combine_fs
after
f1_weighted
nn
[(50,), 3, 10, 0.0001]
1.00
0.57+/-0.25
0.58+/-0.26
0.59+/-0.23
0.44
0.46
0.47
19
combine_fs
after
precision_weighted
nn
[(70, 70, 70), 3, 50, 0.001]
1.00
0.56+/-0.2
0.57+/-0.22
0.58+/-0.19
0.57
0.62
0.79
33
lasso_fs
none
f1_weighted
nn
[(70, 70, 70), 1, 1e-05]
1.00
0.55+/-0.22
0.56+/-0.24
0.57+/-0.21
0.57
0.62
0.79
32
lasso_fs
none
precision_weighted
nn
[(70, 70, 70), 1, 1e-05]
1.00
0.55+/-0.22
0.56+/-0.24
0.57+/-0.21
0.57
0.62
0.79
22
lasso_fs
after
precision_weighted
nn
[(30, 30), 1, 4, 1e-05]
1.00
0.54+/-0.25
0.54+/-0.27
0.56+/-0.23
0.57
0.62
0.79
9
combine_fs
none
f1_weighted
nn
[(70,), 10, 0.0001]
1.00
0.54+/-0.24
0.57+/-0.25
0.57+/-0.22
0.44
0.46
0.47
20
lasso_fs
none
precision_weighted
svmRBF
[0.001, 1, 10, None]
0.86
0.51+/-0.13
0.55+/-0.25
0.61+/-0.09
0.69
0.69
0.69
38
lasso_fs
none
f1_weighted
svmRBF
[0.001, 1, 10, None]
0.86
0.51+/-0.13
0.55+/-0.25
0.61+/-0.09
0.69
0.69
0.69
23
lasso_fs
after
f1_weighted
nn
[(100, 100), 1, 3, 0.001]
1.00
0.49+/-0.2
0.51+/-0.21
0.5+/-0.21
0.57
0.62
0.79
3
combine_fs
none
precision_weighted
svmRBF
[0.1, balanced, 0.5, 20]
0.93
0.47+/-0.17
0.48+/-0.2
0.5+/-0.16
0.67
0.69
0.82
2
combine_fs
none
f1_weighted
svmRBF
[0.1, balanced, 0.5, 20]
0.93
0.47+/-0.17
0.48+/-0.2
0.5+/-0.16
0.67
0.69
0.82
29
lasso_fs
after
f1_weighted
svmRBF
[0.1, 1, 3, 0.5, None]
1.00
0.45+/-0.15
0.46+/-0.2
0.51+/-0.15
0.68
0.69
0.71
28
lasso_fs
after
precision_weighted
svmRBF
[0.1, 1, 3, 0.5, None]
1.00
0.45+/-0.15
0.46+/-0.2
0.51+/-0.15
0.68
0.69
0.71
In [380]:
dfDesc.boxplot(by="cls",column=["cv_f1_mean"])
#sns.swarmplot(x="cls", data=dfDesc.groupby("cls")["cv_f1_mean"].groups, color=".25")
plt.ylim(0.4,1.0)
plt.suptitle("")
plt.xlabel("")
plt.title("")
Out[380]:
<matplotlib.text.Text at 0x7f8794ea4850>
In [381]:
#Pick one randomly (from the group with same f1)
idx = dfDesc.groupby(['cls'])['cv_f1_mean'].transform(max) == dfDesc['cv_f1_mean']
dfDescIdx = dfDesc[idx]
dfDescIdx = dfDescIdx.groupby("cls").first()
dfDescIdx[["fs","sm","metric","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
"test_f1",'test_prec', 'test_rec']]
Out[381]:
fs
sm
metric
params
tr_f1
cv_f1
cv_prec
cv_rec
test_f1
test_prec
test_rec
cls
nn
none
none
f1_weighted
[(30, 30), 0.01]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
svmRBF
none
none
f1_weighted
[0.001, None, 50]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
In [382]:
#Plot performances best classifier algorithms
dfDescIdx[["cv_f1_mean","test_f1"]].plot(kind="bar", yerr=[[0.08,0.09],[0.0,0.0]], rot=0,alpha=0.5)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.xlabel("")
Out[382]:
<matplotlib.text.Text at 0x7f8788d053d0>
In [383]:
idx = dfDesc.groupby(['cls'])['cv_f1_mean'].transform(max) == dfDesc['cv_f1_mean']
dfDesc[idx][["fs","sm","metric","cls","params",
"tr_f1","cv_f1", "cv_prec", "cv_rec",
"test_f1",'test_prec', 'test_rec']]
Out[383]:
fs
sm
metric
cls
params
tr_f1
cv_f1
cv_prec
cv_rec
test_f1
test_prec
test_rec
50
none
none
f1_weighted
svmRBF
[0.001, None, 50]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
43
none
none
precision_weighted
svmRBF
[0.001, None, 50]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
48
none
none
f1_weighted
nn
[(30, 30), 0.01]
1.00
0.71+/-0.18
0.76+/-0.2
0.72+/-0.17
0.76
0.77
0.85
In [384]:
df.to_csv(os.path.join('resources','results',folder,'ds-{}.csv'.format(ds)))
In [385]:
print "Dataset ", ds
print "Total time:", df.time.sum()/3600, "hours"
Dataset 2
Total time: 5.77475739313 hours
In [173]:
print df.fs.unique()
print df.cls.unique()
print df.sm.unique()
print df.metric.unique()
['none' 'rfe_rf_fs' 'combine_fs' 'lasso_fs']
['svmRBF' 'nn' 'logReg' 'rf' 'knn']
['after' 'none']
['f1_weighted' 'precision_weighted']
In [ ]:
Content source: xrafael/readmission
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