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Commit 2f219768 authored by Alessia Marcolini's avatar Alessia Marcolini
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Merge branch 'master' of gitlab.fbk.eu:MPBA/inf_revamped

parents 4296736a fa0ecd8d
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......@@ -11,42 +11,42 @@ LAYER2 = config['layer2']
rule all:
input:
expand("{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_tr_MCC_scores.txt",
outfolder=OUTFOLDER, dataset=DATASET, target=TARGET, layer1=LAYER1, layer2=LAYER2),
expand("{outfolder}/{dataset}/{target}/single/{layer}_tr_MCC_scores.txt",
outfolder=OUTFOLDER, dataset=DATASET, target=TARGET, layer=LAYER1),
expand("{outfolder}/{dataset}/{target}/single/{layer}_tr_MCC_scores.txt",
outfolder=OUTFOLDER, dataset=DATASET, target=TARGET, layer=LAYER2)
expand("{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_tr_MCC_scores.txt",
outfolder=OUTFOLDER, dataset=DATASET, target=TARGET, layer1=LAYER1, layer2=LAYER2, split_id=[i for i in range(10)]),
expand("{outfolder}/{dataset}/{target}/{split_id}/single/{layer}_tr_MCC_scores.txt",
outfolder=OUTFOLDER, dataset=DATASET, target=TARGET, layer=LAYER1, split_id=[i for i in range(10)]),
expand("{outfolder}/{dataset}/{target}/{split_id}/single/{layer}_tr_MCC_scores.txt",
outfolder=OUTFOLDER, dataset=DATASET, target=TARGET, layer=LAYER2, split_id=[i for i in range(10)])
rule ml_juxt_tr:
input:
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer1}_{layer2}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_tr.txt")
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer1}_{layer2}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_tr.txt")
output:
"{outfolder}/{dataset}/{target}/juxt/{layer1}_{layer2}_tr_RandomForest_KBest.log"
"{outfolder}/{dataset}/{target}/{split_id}/juxt/{layer1}_{layer2}_tr_RandomForest_KBest.log"
shell:
"python sklearn_rf_training_fixrank.py {input} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/juxt --ranking KBest"
rule ml_juxt_val:
input:
"{outfolder}/{dataset}/{target}/juxt/{layer1}_{layer2}_tr_RandomForest_KBest.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer1}_{layer2}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_ts.txt")
"{outfolder}/{dataset}/{target}/{split_id}/juxt/{layer1}_{layer2}_tr_RandomForest_KBest.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer1}_{layer2}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_ts.txt")
output:
"{outfolder}/{dataset}/{target}/juxt/{layer1}_{layer2}_tr_MCC_scores.txt"
"{outfolder}/{dataset}/{target}/{split_id}/juxt/{layer1}_{layer2}_tr_MCC_scores.txt"
shell:
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/juxt --tslab {input[2]}"
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/juxt --tslab {input[2]}"
rule snf:
input:
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer1}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}{layer2}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_tr.txt")
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer1}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer2}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_tr.txt")
threads: 8
output:
"{outfolder}/{dataset}/{target}/rSNF/INF_{layer1}_{layer2}_tr.txt"
"{outfolder}/{dataset}/{target}/{split_id}/rSNF/INF_{layer1}_{layer2}_tr.txt"
shell:
"Rscript snf_integration.R --d1 {input[0]} --d2 {input[1]} --lab {input[2]} \
--scriptDir SNFtools/ --clust spectral --threads {threads} \
......@@ -55,85 +55,85 @@ rule snf:
rule ml_rsnf_tr:
input:
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer1}_{layer2}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_tr.txt"),
"{outfolder}/{dataset}/{target}/rSNF/INF_{layer1}_{layer2}_tr.txt"
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer1}_{layer2}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_tr.txt"),
"{outfolder}/{dataset}/{target}/{split_id}/rSNF/INF_{layer1}_{layer2}_tr.txt"
output:
"{outfolder}/{dataset}/{target}/rSNF/{layer1}_{layer2}_tr_RandomForest_rankList.log"
"{outfolder}/{dataset}/{target}/{split_id}/rSNF/{layer1}_{layer2}_tr_RandomForest_rankList.log"
shell:
"python sklearn_rf_training_fixrank.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/rSNF --ranking rankList --rankFeats {input[2]}"
"python sklearn_rf_training_fixrank.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/rSNF --ranking rankList --rankFeats {input[2]}"
rule ml_rsnf_val:
input:
"{outfolder}/{dataset}/{target}/rSNF/{layer1}_{layer2}_tr_RandomForest_rankList.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer1}_{layer2}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_ts.txt")
"{outfolder}/{dataset}/{target}/{split_id}/rSNF/{layer1}_{layer2}_tr_RandomForest_rankList.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer1}_{layer2}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_ts.txt")
output:
"{outfolder}/{dataset}/{target}/rSNF/{layer1}_{layer2}_tr_MCC_scores.txt"
"{outfolder}/{dataset}/{target}/{split_id}/rSNF/{layer1}_{layer2}_tr_MCC_scores.txt"
shell:
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/rSNF --tslab {input[2]}"
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/rSNF --tslab {input[2]}"
rule myintersect:
input:
"{outfolder}/{dataset}/{target}/juxt/{layer1}_{layer2}_tr_RandomForest_KBest.log",
"{outfolder}/{dataset}/{target}/rSNF/{layer1}_{layer2}_tr_RandomForest_rankList.log"
"{outfolder}/{dataset}/{target}/{split_id}/juxt/{layer1}_{layer2}_tr_RandomForest_KBest.log",
"{outfolder}/{dataset}/{target}/{split_id}/rSNF/{layer1}_{layer2}_tr_RandomForest_rankList.log"
output:
"{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_intersect_tr.txt"
"{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_intersect_tr.txt"
shell:
"python intersect_biomarkers.py {input} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/rSNFi/venn_{wildcards.layer1}_{wildcards.layer2}_tr.png {output} --title1 {wildcards.layer1} --title2 {wildcards.layer2}"
"python intersect_biomarkers.py {input} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/rSNFi/venn_{wildcards.layer1}_{wildcards.layer2}_tr.png {output} --title1 {wildcards.layer1} --title2 {wildcards.layer2}"
rule extract:
input:
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer1}_{layer2}_tr.txt"),
"{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_intersect_tr.txt"
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer1}_{layer2}_tr.txt"),
"{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_intersect_tr.txt"
output:
"{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_tr.txt"
"{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_tr.txt"
shell:
"python extract_topfeats_onecol.py {input} {output}"
rule ml_rsnfi_tr:
input:
"{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_tr.txt",
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_tr.txt")
"{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_tr.txt",
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_tr.txt")
output:
"{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_tr_RandomForest_KBest.log"
"{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_tr_RandomForest_KBest.log"
shell:
"python sklearn_rf_training_fixrank.py {input} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/rSNFi --ranking KBest"
"python sklearn_rf_training_fixrank.py {input} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/rSNFi --ranking KBest"
rule ml_rsnfi_val:
input:
"{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_tr_RandomForest_KBest.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer1}_{layer2}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_ts.txt")
"{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_tr_RandomForest_KBest.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer1}_{layer2}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_ts.txt")
output:
"{outfolder}/{dataset}/{target}/rSNFi/{layer1}_{layer2}_tr_MCC_scores.txt"
"{outfolder}/{dataset}/{target}/{split_id}/rSNFi/{layer1}_{layer2}_tr_MCC_scores.txt"
shell:
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/rSNFi --tslab {input[2]}"
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/rSNFi --tslab {input[2]}"
rule single_tr:
input:
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_tr.txt")
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer}_tr.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_tr.txt")
output:
"{outfolder}/{dataset}/{target}/single/{layer}_tr_RandomForest_KBest.log"
"{outfolder}/{dataset}/{target}/{split_id}/single/{layer}_tr_RandomForest_KBest.log"
shell:
"python sklearn_rf_training_fixrank.py {input} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/single --ranking KBest"
"python sklearn_rf_training_fixrank.py {input} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/single --ranking KBest"
rule single_val:
input:
"{outfolder}/{dataset}/{target}/single/{layer}_tr_RandomForest_KBest.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{layer}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/labels_{target}_ts.txt")
"{outfolder}/{dataset}/{target}/{split_id}/single/{layer}_tr_RandomForest_KBest.log",
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/{layer}_ts.txt"),
os.path.join(DATAFOLDER, "{dataset}/{target}/{split_id}/labels_{target}_ts.txt")
output:
"{outfolder}/{dataset}/{target}/single/{layer}_tr_MCC_scores.txt"
"{outfolder}/{dataset}/{target}/{split_id}/single/{layer}_tr_MCC_scores.txt"
shell:
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/single --tslab {input[2]}"
"python sklearn_rf_validation_writeperf.py {input[0]} {input[1]} {wildcards.outfolder}/{wildcards.dataset}/{wildcards.target}/{wildcards.split_id}/single --tslab {input[2]}"
# rule single_layer:
......
sklearn_rf_training_fixrank.py
View file @ 2f219768
......@@ -97,7 +97,7 @@ y_orig = np.loadtxt(LABELSFILE, dtype=np.int)
# encode labels
le = preprocessing.LabelEncoder()
y = le.fit_transform(y_orig)
is_multiclass = len(le.classes_) > 2
# If ranked list is given as input to DAP, read it and extract features index
if RANK_METHOD == "rankList":
rankedList = np.loadtxt(RANKFEATS, delimiter='\t', dtype=str, skiprows=1)
......@@ -138,7 +138,11 @@ else:
for i in range(CV_N):
ys.append(y)
cvmetrics_df = pd.DataFrame(columns=['Iteration', 'Fold', 'nf', 'mcc', 'sens', 'spec', 'acc', 'auc', 'npv', 'ppv'])
if is_multiclass:
cvmetrics_df = pd.DataFrame(columns=['Iteration', 'Fold', 'nf', 'mcc', 'auc'])
lb = preprocessing.LabelBinarizer()
else:
cvmetrics_df = pd.DataFrame(columns=['Iteration', 'Fold', 'nf', 'mcc', 'sens', 'spec', 'acc', 'auc', 'npv', 'ppv'])
for n in range(CV_N):
skf = StratifiedKFold(CV_K, shuffle=True, random_state=n)
......@@ -175,20 +179,35 @@ for n in range(CV_N):
RANKING[(n * CV_K) + i] = ranking_tmp
if is_multiclass:
forest_mc = OneVsRestClassifier(RandomForestClassifier(n_estimators=500, criterion='gini', random_state=n, n_jobs=1))
y_tr_bin = lb.fit_transform(y_tr)
y_ts_bin = lb.transform(y_ts)
for j, s in enumerate(FSTEPS):
v = RANKING[(n * CV_K) + i][:s]
x_tr_fs, x_ts_fs = x_tr[:, v], x_ts[:, v]
forest.fit(x_tr_fs, y_tr)
yp = forest.predict(x_ts_fs)
cvmetrics_df = cvmetrics_df.append({'Iteration': n, 'Fold': i, 'nf': s,
'mcc': matthews_corrcoef(y_ts, yp),
'sens': perf.sensitivity(y_ts, yp),
'spec': perf.specificity(y_ts, yp),
'acc': accuracy_score(y_ts, yp),
'auc': roc_auc_score(y_ts, yp),
'npv': perf.npv(y_ts, yp),
'ppv': perf.ppv(y_ts, yp)}, ignore_index=True)
if is_multiclass:
forest_mc.fit(x_tr_fs, y_tr_bin)
yp_bin = forest.predict(x_ts_fs)
cvmetrics_df = cvmetrics_df.append({'Iteration': n, 'Fold': i, 'nf': s,
'mcc': matthews_corrcoef(y_ts, yp),
'auc': roc_auc_score(y_ts_bin, yp_bin, average="micro")}, ignore_index=True)
else:
cvmetrics_df = cvmetrics_df.append({'Iteration': n, 'Fold': i, 'nf': s,
'mcc': matthews_corrcoef(y_ts, yp),
'sens': perf.sensitivity(y_ts, yp),
'spec': perf.specificity(y_ts, yp),
'acc': accuracy_score(y_ts, yp),
'auc': roc_auc_score(y_ts, yp),
'npv': perf.npv(y_ts, yp),
'ppv': perf.ppv(y_ts, yp)}, ignore_index=True)
cvmetrics_df[['Iteration', 'Fold', 'nf']] = cvmetrics_df[['Iteration', 'Fold', 'nf']].astype(int)
......@@ -201,23 +220,28 @@ np.savetxt(OUTFILE + "_ranking.csv.gz", RANKING, fmt='%d', delimiter='\t')
avg_df = cvmetrics_df.groupby(['nf']).mean()
# confidence intervals
AMCC, ASENS, ASPEC, AACC, AAUC, ANPV, APPV = [avg_df[metric].values for metric in ['mcc', 'sens', 'spec', 'acc', 'auc', 'npv', 'ppv']]
MCC_CI, SENS_CI, SPEC_CI, ACC_CI, AUC_CI, NPV_CI, PPV_CI = ([] for i in range(7))
if is_multiclass:
AMCC, AAUC = [avg_df[metric].values for metric in ['mcc', 'auc']]
MCC_CI, AUC_CI = ([] for i in range(2))
else:
AMCC, ASENS, ASPEC, AACC, AAUC, ANPV, APPV = [avg_df[metric].values for metric in ['mcc', 'sens', 'spec', 'acc', 'auc', 'npv', 'ppv']]
MCC_CI, SENS_CI, SPEC_CI, ACC_CI, AUC_CI, NPV_CI, PPV_CI = ([] for i in range(7))
for nf in FSTEPS:
res = bs.bootstrap(cvmetrics_df['mcc'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
MCC_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['sens'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
SENS_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['spec'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
SPEC_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['acc'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
ACC_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['auc'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
AUC_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['npv'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
NPV_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['ppv'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
PPV_CI.append([res.lower_bound, res.upper_bound])
if not is_multiclass:
res = bs.bootstrap(cvmetrics_df['sens'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
SENS_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['spec'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
SPEC_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['acc'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
ACC_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['npv'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
NPV_CI.append([res.lower_bound, res.upper_bound])
res = bs.bootstrap(cvmetrics_df['ppv'][cvmetrics_df['nf']==nf].values, stat_func=bs_stats.mean)
PPV_CI.append([res.lower_bound, res.upper_bound])
# Borda list
BORDA_ID, _, BORDA_POS = borda_count(RANKING)
......@@ -232,11 +256,19 @@ for ss in FSTEPS:
metrics_array = np.empty((len(FSTEPS), 22))
for j,s in enumerate(FSTEPS):
metrics_array[j] = np.array([s, AMCC[j], MCC_CI[j][0], MCC_CI[j][1], ASENS[j], SENS_CI[j][0], SENS_CI[j][1],
ASPEC[j], SPEC_CI[j][0], SPEC_CI[j][1], AACC[j], ACC_CI[j][0], ACC_CI[j][1],
AAUC[j], AUC_CI[j][0], AUC_CI[j][1], ANPV[j], NPV_CI[j][0], NPV_CI[j][1],
APPV[j], PPV_CI[j][0], PPV_CI[j][1]])
metrics_df = pd.DataFrame(metrics_array, columns=["nf", "mcc", "mcc_min", "mcc_max", "sens", "sens_min", "sens_max", "spec", "spec_min", "spec_max", "acc", "acc_min", "acc_max", "auc", "auc_min", "auc_max", "npv", "npv_min", "npv_max", "ppv", "ppv_min", "ppv_max"])
if is_multiclass:
metrics_array[j] = np.array([s, AMCC[j], MCC_CI[j][0], MCC_CI[j][1],
AAUC[j], AUC_CI[j][0], AUC_CI[j][1]])
else:
metrics_array[j] = np.array([s, AMCC[j], MCC_CI[j][0], MCC_CI[j][1], ASENS[j], SENS_CI[j][0], SENS_CI[j][1],
ASPEC[j], SPEC_CI[j][0], SPEC_CI[j][1], AACC[j], ACC_CI[j][0], ACC_CI[j][1],
AAUC[j], AUC_CI[j][0], AUC_CI[j][1], ANPV[j], NPV_CI[j][0], NPV_CI[j][1],
APPV[j], PPV_CI[j][0], PPV_CI[j][1]])
if is_multiclass:
metrics_df = pd.DataFrame(metrics_array, columns=["nf", "mcc", "mcc_min", "mcc_max", "auc", "auc_min", "auc_max"])
else:
metrics_df = pd.DataFrame(metrics_array, columns=["nf", "mcc", "mcc_min", "mcc_max", "sens", "sens_min", "sens_max", "spec", "spec_min", "spec_max", "acc", "acc_min", "acc_max", "auc", "auc_min", "auc_max", "npv", "npv_min", "npv_max", "ppv", "ppv_min", "ppv_max"])
metrics_df['nf'] = metrics_df['nf'].astype(int)
metrics_df.to_csv(f"{OUTFILE}_metrics.txt", sep='\t', index=False, float_format="%.3f")
......
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