Python loadPredictions Exemples

Exemple #1

Fichier : genEns_BagsSubjects.py Projet : Keesiu/meta-kaggle

subjects_test = ids[:, 1]
series_test = ids[:, 2]
ids = ids[:, 0]
labels = np.load('../infos_val.npy')
subjects = labels[:, -2]
series = labels[:, -1]
labels = labels[:, :-2]

allCols = list(range(len(cols)))

# ## loading prediction ###
files = getLvl1ModelList()

preds_val = OrderedDict()
for f in files:
    loadPredictions(preds_val, f[0], f[1])
# validity check
for m in ensemble:
    assert(m in preds_val)

# ## train/test ###
aggr = createEnsFunc(ensemble)
dataTrain = aggr(preds_val)
preds_val = None

# switch to add subjects
if addSubjectID:
    dataTrain = np.c_[dataTrain, subjects]

np.random.seed(4234521)

Exemple #2

Fichier : genEns_BagsSubjects.py Projet : SherazKhan/Grasp-and-lift-EEG-challenge

subjects_test = ids[:, 1]
series_test = ids[:, 2]
ids = ids[:, 0]
labels = np.load('../infos_val.npy')
subjects = labels[:, -2]
series = labels[:, -1]
labels = labels[:, :-2]

allCols = range(len(cols))

# ## loading prediction ###
files = getLvl1ModelList()

preds_val = OrderedDict()
for f in files:
    loadPredictions(preds_val, f[0], f[1])
# validity check
for m in ensemble:
    assert(m in preds_val)

# ## train/test ###
aggr = createEnsFunc(ensemble)
dataTrain = aggr(preds_val)
preds_val = None

# switch to add subjects
if addSubjectID:
    dataTrain = np.c_[dataTrain, subjects]

np.random.seed(4234521)

Exemple #3

######
cols = getEventNames()

labels = np.load('../infos_val.npy')
subjects = labels[:, -2]
series = labels[:, -1]
labels = labels[:, :-2]

allCols = range(len(cols))

# ## loading prediction ###
files = getLvl1ModelList()

preds_val = OrderedDict()
for f in files:
    loadPredictions(preds_val, f[0], f[1])
# validity check
for m in ensemble:
    assert (m in preds_val)

# ## train/test ###
aggr = createEnsFunc(ensemble)
dataTrain = aggr(preds_val)
preds_val = None

# switch to add subjects
if addSubjectID:
    dataTrain = np.c_[dataTrain, subjects]

np.random.seed(seed)

Exemple #4

Fichier : genFinal.py Projet : Keesiu/meta-kaggle

ids = np.load('../infos_test.npy')
subjects_test = ids[:, 1]
series_test = ids[:, 2]
ids = ids[:, 0]
labels = np.load('../infos_val.npy')
subjects = labels[:, -2]
series = labels[:, -1]
labels = labels[:, :-2]

allCols = list(range(len(cols)))

# ## loading prediction ###
files = ensemble
preds_val = OrderedDict()
for f in files:
    loadPredictions(preds_val, f, [f], lvl=2)

# ## train/test ###
aggr = createEnsFunc(ensemble)
dataTrain = aggr(preds_val)
preds_val = None

# do CV
aucs = []
cv = LeaveOneLabelOut(series)
p = np.zeros(labels.shape)
for train, test in cv:
    currentSeries = np.unique(series[test])[0]
    for m in range(len(models)):
        models[m].fit(dataTrain[train][::subsample],
                      labels[train][::subsample])

Exemple #5

Fichier : genFinal.py Projet : SherazKhan/Grasp-and-lift-EEG-challenge

ids = np.load('../infos_test.npy')
subjects_test = ids[:, 1]
series_test = ids[:, 2]
ids = ids[:, 0]
labels = np.load('../infos_val.npy')
subjects = labels[:, -2]
series = labels[:, -1]
labels = labels[:, :-2]

allCols = range(len(cols))

# ## loading prediction ###
files = ensemble
preds_val = OrderedDict()
for f in files:
    loadPredictions(preds_val, f, [f], lvl=2)

# ## train/test ###
aggr = createEnsFunc(ensemble)
dataTrain = aggr(preds_val)
preds_val = None

# do CV
aucs = []
cv = LeaveOneLabelOut(series)
p = np.zeros(labels.shape)
for train,test in cv:
    currentSeries = np.unique(series[test])[0]
    for m in range(len(models)):
        models[m].fit(dataTrain[train][::subsample], labels[train][::subsample])
        p[test] += models[m].predict_proba(dataTrain[test]) / len(mean_type)