def predict(dir, X_pred):
if not os.path.exists(dir + '.hdf5'):
raise IOError(
'Directory {:s} does not contain proper random forest!\n'.format(
dir))
predictions = np.zeros(len(X_pred))
X_pred = np.asarray(X_pred)
trunks = []
compares = []
leafs = []
with h5py.File(dir + '.hdf5', "r") as h5f:
trees = h5f.keys()
random.shuffle(trees)
trees = trees[:int(len(trees) * treefraction)]
for t in trees:
trunks.append(h5f[t + '/trunks'][()])
compares.append(h5f[t + '/compares'][()])
leafs.append(h5f[t + '/leafs'][()])
shape = (len(trunks), max(t.shape[0] for t in trunks),
max(t.shape[1] for t in trunks))
trunks_ = np.full(shape, np.nan, dtype=np.int64)
for i, t in enumerate(trunks):
trunks_[i, :t.shape[0], :t.shape[1]] = t
leafs_ = np.full((len(leafs), max(l.shape[0] for l in leafs)),
np.nan,
dtype=np.float64)
for i, t in enumerate(leafs):
leafs_[i, :t.shape[0]] = t
compares_ = np.full((len(compares), max(c.shape[0] for c in compares)),
np.nan,
dtype=np.float64)
for i, t in enumerate(compares):
compares_[i, :t.shape[0]] = t
del leafs, trunks, compares
_predict_parallel.predict(trunks_,
leafs_,
compares_,
X_pred,
predictions,
num_threads=num_threads)
print predictions
return predictions
def predict(dir, X_pred):
if not os.path.exists(dir + '/tree.list'):
raise IOError('Directory {:s} does not contain proper random forest!\n'.format(dir))
trees = open(dir + '/tree.list').read().strip().split('\n')
random.shuffle(trees)
trees = trees[:int(len(trees)*treefraction)]
predictions = np.zeros(len(X_pred))
X_pred = np.asarray(X_pred)
trunks = []
compares = []
leafs = []
for t in trees:
t = joblib.load(dir + '/' + t.split('/')[-1])[:5]
trunks.append(np.vstack((t[0], t[2:4])))
compares.append(t[1])
leaf = t[4][:, 0, :]
leafs.append(leaf[:, 1] / leaf.sum(axis=1))
shape = (len(trunks), max(t.shape[0] for t in trunks), max(t.shape[1] for t in trunks))
trunks_ = np.full(shape, np.nan, dtype=np.int64)
for i, t in enumerate(trunks):
trunks_[i, :t.shape[0], :t.shape[1]] = t
leafs_ = np.full((len(leafs), max(l.shape[0] for l in leafs)), np.nan, dtype=np.float64)
for i, t in enumerate(leafs):
leafs_[i, :t.shape[0]] = t
compares_ = np.full((len(compares), max(c.shape[0] for c in compares)), np.nan, dtype=np.float64)
for i, t in enumerate(compares):
compares_[i, :t.shape[0]] = t
del leafs, trunks, compares
_predict_parallel.predict(trunks_, leafs_, compares_, X_pred, predictions, num_threads=num_threads)
print predictions
return predictions
def predict(dir, X_pred):
if not os.path.exists(dir + "/tree.list"):
raise IOError("Directory {:s} does not contain proper random forest!\n".format(dir))
trees = open(dir + "/tree.list").read().strip().split("\n")
random.shuffle(trees)
trees = trees[: int(len(trees) * treefraction)]
predictions = np.zeros(len(X_pred))
X_pred = np.asarray(X_pred)
trunks = []
compares = []
leafs = []
for t in trees:
t = joblib.load(dir + "/" + t.split("/")[-1])[:5]
trunks.append(np.vstack((t[0], t[2:4])))
compares.append(t[1])
leaf = t[4][:, 0, :]
leafs.append(leaf[:, 1] / leaf.sum(axis=1))
shape = (len(trunks), max(t.shape[0] for t in trunks), max(t.shape[1] for t in trunks))
trunks_ = np.full(shape, np.nan, dtype=np.int64)
for i, t in enumerate(trunks):
trunks_[i, : t.shape[0], : t.shape[1]] = t
leafs_ = np.full((len(leafs), max(l.shape[0] for l in leafs)), np.nan, dtype=np.float64)
for i, t in enumerate(leafs):
leafs_[i, : t.shape[0]] = t
compares_ = np.full((len(compares), max(c.shape[0] for c in compares)), np.nan, dtype=np.float64)
for i, t in enumerate(compares):
compares_[i, : t.shape[0]] = t
del leafs, trunks, compares
_predict_parallel.predict(trunks_, leafs_, compares_, X_pred, predictions, num_threads=num_threads)
print predictions
return predictions
def predict(dir, X_pred):
if not os.path.exists(dir + '.hdf5'):
raise IOError('Directory {:s} does not contain proper random forest!\n'.format(dir))
predictions = np.zeros(len(X_pred))
X_pred = np.asarray(X_pred)
trunks = []
compares = []
leafs = []
with h5py.File(dir + '.hdf5', "r") as h5f:
trees = h5f.keys()
random.shuffle(trees)
trees = trees[:int(len(trees)*treefraction)]
for t in trees:
trunks.append(h5f[t + '/trunks'][()])
compares.append(h5f[t + '/compares'][()])
leafs.append(h5f[t + '/leafs'][()])
shape = (len(trunks), max(t.shape[0] for t in trunks), max(t.shape[1] for t in trunks))
trunks_ = np.full(shape, np.nan, dtype=np.int64)
for i, t in enumerate(trunks):
trunks_[i, :t.shape[0], :t.shape[1]] = t
leafs_ = np.full((len(leafs), max(l.shape[0] for l in leafs)), np.nan, dtype=np.float64)
for i, t in enumerate(leafs):
leafs_[i, :t.shape[0]] = t
compares_ = np.full((len(compares), max(c.shape[0] for c in compares)), np.nan, dtype=np.float64)
for i, t in enumerate(compares):
compares_[i, :t.shape[0]] = t
del leafs, trunks, compares
_predict_parallel.predict(trunks_, leafs_, compares_, X_pred, predictions, num_threads=num_threads)
print predictions
return predictions
