def samples_per_leaf_node_ensemble(meta_m, m):
path_out_tr = r"/home/irene/PycharmProjects/04_Risk_Model/data/skewed_leaves/samples_leaf_node_ensemble_train.csv"
path_out_te = r"/home/irene/PycharmProjects/04_Risk_Model/data/skewed_leaves/samples_leaf_node_ensemble_test.csv"
Y = m[:, 0]
X = m[:, 1:]
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(X, Y, meta_m, train_size=0.60,
random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
n_estimators = [50]
samples_per_leaf = [1000]
header = ["ntree", "nnode", "tb_per_px"]
with open(path_out_tr, "w", newline="") as wtr:
with open(path_out_te, "w", newline="") as wte:
writer_tr = csv.writer(wtr, delimiter=";")
writer_tr.writerow(header)
writer_te = csv.writer(wte, delimiter=";")
writer_te.writerow(header)
for spl in samples_per_leaf:
for n_esti in n_estimators:
print()
print("Analysis: RF with Skewed Leaves")
print("Samples per leaf node: ", spl)
print("Number of estimators: ", n_esti)
print("-" * 50)
ensemble = RandomForestRegressor(n_estimators=n_esti, min_samples_leaf=spl, bootstrap=True)
ensemble.fit(xtrain, ytrain)
leaves_train = ensemble.apply(xtrain)
dicori_train = samples_per_leaf_node(leaves_train, xtrain, ytrain)
l = []
for key in sorted(dicori_train.keys()):
for sam in dicori_train[key]:
l.append(sam[0])
newrow = [key[0], key[1]] + l
writer_tr.writerow(newrow)
l = []
leaves_test = ensemble.apply(xtest)
dicori_test = samples_per_leaf_node(leaves_test, xtest, ytest)
l = []
for key in sorted(dicori_test.keys()):
for sam in dicori_test[key]:
l.append(sam[0])
newrow = [key[0], key[1]] + l
writer_te.writerow(newrow)
l = []
class stack_model(BaseEstimator):
def __init__(self, cols1 = None, cols2 = None):
self.rf_stack = RandomForestRegressor(n_estimators=100, max_features=None, max_depth = 5, min_impurity_decrease = 0.0, min_samples_split = 10, min_samples_leaf=10, bootstrap=True, random_state=42)
self.lm_stack = Lasso(alpha=0.001, normalize=True, max_iter=1000, random_state=42)
self.cols1 = cols1
self.cols2 = cols2
self.lm_models = {}
def get_params(self, deep=True):
return {'cols1' : self.cols1, 'cols2' : self.cols2}
def set_params(self, **parameters):
self.cols1 = parameters['cols1']
self.cols2 = parameters['cols2']
return self
def fit(self, df, y):
cols1 = list(df.columns) if self.cols1 is None else self.cols1
cols2 = list(df.columns) if self.cols2 is None else self.cols2
self.rf_stack.fit(df[cols1],y)
leaf = self.rf_stack.apply(df[cols1])
#one lm model for every rf estimator and leaf
for f_idx in range(leaf.shape[1]):
for leaf_num, idxs in pd.DataFrame(leaf[:,f_idx]).reset_index().groupby(0):
idxs = idxs['index'].values
df_leaf = df[cols2].iloc[idxs].copy()
y_leaf = y.iloc[idxs].copy()
lm_model = clone(self.lm_stack)
lm_model.fit(df_leaf,y_leaf)
self.lm_models[(f_idx, leaf_num)] = lm_model
return self
def predict(self, df):
cols1 = list(df.columns) if self.cols1 is None else self.cols1
cols2 = list(df.columns) if self.cols2 is None else self.cols2
leaf = self.rf_stack.apply(df[cols1])
stack_preds = np.zeros_like(leaf, dtype=float)
#predict unsing lm models for every rf estimator and leaf
for f_idx in range(leaf.shape[1]):
for leaf_num, idxs in pd.DataFrame(leaf[:,f_idx]).reset_index().groupby(0):
idxs = idxs['index'].values
df_leaf = df[cols2].iloc[idxs].copy()
lm_model = self.lm_models[(f_idx, leaf_num)]
leaf_pred = lm_model.predict(df_leaf)
stack_preds[idxs,f_idx] = leaf_pred
y_pred = stack_preds.mean(axis = 1)
return y_pred
#check_estimator(stack_model)
def _data_clusterings(self, data_z, data_p, data_y):
''' Returns the centers and precisions of an epsilon cover of gaussians.
Currently the centers are just an epsilon grid and the precisions 1/(3*epsilon),
i.e. the standard deviation is 3 times the distance between two grid points.
Later this is exactly the function that will be implementing the tree style splitting.
and returning a more tailored to the data epsilon cover.'''
if self._cluster_type == 'forest':
from sklearn.ensemble import RandomForestRegressor
dtree = RandomForestRegressor(n_estimators=self._num_trees, max_leaf_nodes=self._n_critics, min_samples_leaf=self._min_cluster_size)
dtree.fit(data_z, data_p)
cluster_labels = dtree.apply(data_z)
#dtree.fit(data_z, data_y)
#cluster_labels = np.concatenate((cluster_labels, dtree.apply(data_z)), axis=1)
cluster_ids = [np.unique(cluster_labels[:, c]) for c in range(cluster_labels.shape[1])]
elif self._cluster_type == 'kmeans':
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=self._n_critics).fit(data_z)
cluster_labels = kmeans.labels_.reshape(-1, 1)
cluster_ids = [np.unique(cluster_labels)]
elif self._cluster_type == 'random_points':
center_ids = np.random.choice(np.arange(data_z.shape[0]), size=self._n_critics, replace=False)
cluster_labels = np.zeros((data_z.shape[0], self._n_critics))
cluster_ids = np.ones((self._n_critics, 1))
for it, center in enumerate(center_ids):
distances = np.linalg.norm(data_z - data_z[center], axis=1)
cluster_members = np.argsort(distances)[:self._min_cluster_size]
cluster_labels[cluster_members, it] = 1
else:
raise Exception("Unknown option {}".format(self._cluster_type))
#z_min = np.percentile(data_z, 0) - self._epsilon
#z_max = np.percentile(data_z, 100) + self._epsilon
#center_grid = np.arange(z_min, z_max, self._epsilon)
#precision_grid = np.ones(center_grid.shape[0]) / (3 * self._epsilon)
return cluster_labels, cluster_ids
def test_varying_samples_per_node(meta_m, m):
print("Type m: ", m.dtype)
path_out = r"/home/irene/PycharmProjects/04_Risk_Model/data/poisson_leaves/prediction_ytest.csv"
Y = m[:, 0]
X = m[:, 1:]
# Ynz, Xnz = trim_value(Y, X, 0)
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=42)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
n_estimators = [1, 5, 10, 50, 100]
samples_per_leaf = range(100, 1600, 100)
start_all = time.time()
for spl in samples_per_leaf:
start_it = time.time()
for n_esti in n_estimators:
print()
print("Analysis: RF with Poisson Leaves")
print("Samples per leaf node: ", spl)
print("Number of estimators: ", n_esti)
print("-" * 50)
ensemble = RandomForestRegressor(n_estimators=n_esti,
min_samples_leaf=spl,
bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
pred_rf = ensemble.predict(xtest)
pred = predicting_four_models_leaf_nodes(spl, n_esti, ytest, xtest,
pack, pred_rf).T
stack = np.hstack((meta_m_test, pred))
dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
stop_it = time.time()
print("--- Iteration elapsed {0} minutes ---".format(
np.divide(stop_it - start_it, 60)))
end_all = time.time()
print("--- Full program elapsed {0} hours ---".format(
np.divide(end_all - start_all, 3600)))
def test_drf_regressor_backupsklearn(backend='auto'):
df = pd.read_csv("./open_data/simple.txt", delim_whitespace=True)
X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
import h2o4gpu
Solver = h2o4gpu.RandomForestRegressor
#Run h2o4gpu version of RandomForest Regression
drf = Solver(backend=backend, random_state=1234, oob_score=True)
print("h2o4gpu fit()")
drf.fit(X, y)
#Run Sklearn version of RandomForest Regression
from sklearn.ensemble import RandomForestRegressor
drf_sk = RandomForestRegressor(random_state=1234,
oob_score=True,
max_depth=3)
print("Scikit fit()")
drf_sk.fit(X, y)
if backend == "sklearn":
assert (drf.predict(X) == drf_sk.predict(X)).all() == True
assert (drf.score(X, y) == drf_sk.score(X, y)).all() == True
assert (drf.decision_path(X)[1] == drf_sk.decision_path(X)[1]
).all() == True
assert (drf.apply(X) == drf_sk.apply(X)).all() == True
print("Estimators")
print(drf.estimators_)
print(drf_sk.estimators_)
print("n_features")
print(drf.n_features_)
print(drf_sk.n_features_)
assert drf.n_features_ == drf_sk.n_features_
print("n_outputs")
print(drf.n_outputs_)
print(drf_sk.n_outputs_)
assert drf.n_outputs_ == drf_sk.n_outputs_
print("Feature importance")
print(drf.feature_importances_)
print(drf_sk.feature_importances_)
assert (drf.feature_importances_ == drf_sk.feature_importances_
).all() == True
print("oob_score")
print(drf.oob_score_)
print(drf_sk.oob_score_)
assert drf.oob_score_ == drf_sk.oob_score_
print("oob_prediction")
print(drf.oob_prediction_)
print(drf_sk.oob_prediction_)
assert (drf.oob_prediction_ == drf_sk.oob_prediction_).all() == True
def prox_matrix(df, y, features, cluster_dimension, trees=10):
#https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#prox
#initialize datframe for independant variables
independant = pd.DataFrame()
#Handle Categoricals: This should really be added to RandomForestRegressor
for column, data_type in df[features].dtypes.iteritems():
try:
independant[column] = pd.to_numeric(df[column], downcast='integer')
except ValueError:
contains_nulls = df[column].isnull().values.any()
dummies = pd.get_dummies(df[column],
prefix=column,
dummy_na=contains_nulls,
drop_first=True)
independant[dummies.columns] = dummies
if len(independant.index) != len(df.index):
raise Exception('independant variables not stored properly')
#train Model
clf = RandomForestRegressor(n_estimators=trees, n_jobs=-1)
clf.fit(independant, y)
#Final leaf for each tree
leaves = clf.apply(independant)
#value in cluster dimension
labels = df[cluster_dimension].values
numerator_matrix = {}
for i, value_i in enumerate(labels):
for j, value_j in enumerate(labels):
if i >= j:
numerator_matrix[(value_i, value_j)] = numerator_matrix.get(
(value_i, value_j),
0) + np.count_nonzero(leaves[i] == leaves[j])
numerator_matrix[(value_j,
value_i)] = numerator_matrix[(value_i,
value_j)]
#normalize by the total number of possible matchnig leaves
prox_matrix = {
key: 1.0 - float(x) / (trees * np.count_nonzero(labels == key[0]) *
np.count_nonzero(labels == key[1]))
for key, x in numerator_matrix.items()
}
#make sorted dataframe
levels = np.unique(labels)
D = pd.DataFrame(data=[[prox_matrix[(i, j)] for i in levels]
for j in levels],
index=levels,
columns=levels)
return D
def GetBinFeatures(stage, train_imgs, train_shapes, train_bboxes, mean_rshape, targets):
bin_features=[]
forests=[]
random_poses=[]
for ilandmark in range(param_landmark_num):
t1=time.time()
#### get locations
feature_pair_pos=np.zeros((param_local_feature_num[stage]*2, 2))
for i in range(param_local_feature_num[stage]):
while True:
pair=np.random.rand(4)*2-1
x1,y1,x2,y2=pair
if x1*x1+y1*y1<1 and x2*x2+y2*y2<1 and (x1, y1)!=(x2, y2):
break
feature_pair_pos[2*i:2*i+2]=(pair*param_local_radius[stage]).reshape((2,2))
random_poses.append(feature_pair_pos)
#### get pixel difference
features=np.zeros((len(train_shapes), param_local_feature_num[stage]))
for i in range(len(train_shapes)):
#origin_img=cv2.imread(train_imgs[i], 0).astype(np.float)
origin_img=train_imgs[i]
# transform from mean space to current training space
sim_trans=transform.estimate_transform('similarity', CenterShape(mean_rshape), CenterShape(Shape2Relative(train_shapes[i], train_bboxes[i])))
#trans_feature_pair_pos=Shape2Absolute(sim_trans(feature_pair_pos), train_bboxes[i])+train_shapes[i][ilandmark]
trans_feature_pair_pos=GetLocalFeatureAbsolutePos(sim_trans(feature_pair_pos), train_bboxes[i], train_shapes[i][ilandmark]).astype(np.int)
#trans_feature_pair_pos=trans_feature_pair_pos.astype(np.int)
for j in range(param_local_feature_num[stage]):
x1,y1=trans_feature_pair_pos[2*j]
x2,y2=trans_feature_pair_pos[2*j+1]
# in case out of boundary
x1=max(0, min(origin_img.shape[1]-1, x1))
x2=max(0, min(origin_img.shape[1]-1, x2))
y1=max(0, min(origin_img.shape[0]-1, y1))
y2=max(0, min(origin_img.shape[0]-1, y2))
features[i,j]=origin_img[y1,x1] - origin_img[y2,x2]
#del origin_img
#gc.collect()
#### train random forest
forest=RandomForestRegressor(max_depth=param_tree_depth, n_estimators=param_tree_num, n_jobs=8)
forest.fit(features, targets[:, ilandmark])
forests.append(forest)
#### extract binary features for every training sample
leaves, leaves_num=GetLeaves(forest)
reach_nodes=forest.apply(features)
landmark_bin_features=np.zeros((len(train_shapes), leaves_num))
for i in range(len(train_shapes)):
begin_leaf_ind=0
for j in range(len(leaves)):
node=reach_nodes[i, j]
landmark_bin_features[i][begin_leaf_ind+leaves[j][node]]=1
begin_leaf_ind+=len(leaves[j])
bin_features.append(landmark_bin_features)
print('landmark:', ilandmark+1, 'use:', time.time()-t1, 's')
return np.hstack(bin_features), forests, random_poses
def predict_models(meta_m, m, meta_p, p):
print("Type m: ", m.dtype)
path_out = r"/home/irene/PycharmProjects/04_Risk_Model/data/poisson_leaves/prediction_nl_four_models.csv"
Y = m[:, 0]
X = m[:, 1:]
n_esti = 5
spl = 800
# Ynz, Xnz = trim_value(Y, X, 0)
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=0)
ensemble = RandomForestRegressor(n_estimators=n_esti,
min_samples_leaf=spl,
bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
print("Weirdos?")
print(np.isnan(p).any(), np.isinf(p).any(), np.isneginf(p).any())
# pred = predicting_four_models_leaf_nodes(spl, n_esti, ytest, xtest, pack, pred_rf).T
print("Prediction with the four models")
pred = predicting_four_models_leaf_nodes_nl(p, pack)
print("Predicting with random forest")
pred_rf = ensemble.predict(p).reshape(-1, 1)
print(meta_p.shape, pred.T.shape, pred_rf.shape)
stack = np.hstack((meta_p, pred.T, pred_rf))
print(stack.shape, meta_p.shape, pred.shape)
# dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
with open(path_out, "w", newline="") as w:
writer = csv.writer(w, delimiter=";")
for item in stack:
writer.writerow(item)
placed_list = place(stack)
write_tif(placed_list, spl, n_esti)
def test_drf_regressor_backupsklearn(backend='auto'):
df = pd.read_csv("./open_data/simple.txt", delim_whitespace=True)
X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
import h2o4gpu
Solver = h2o4gpu.RandomForestRegressor
#Run h2o4gpu version of RandomForest Regression
drf = Solver(backend=backend, random_state=1234, oob_score=True)
print("h2o4gpu fit()")
drf.fit(X, y)
#Run Sklearn version of RandomForest Regression
from sklearn.ensemble import RandomForestRegressor
drf_sk = RandomForestRegressor(random_state=1234, oob_score=True, max_depth=3)
print("Scikit fit()")
drf_sk.fit(X, y)
if backend == "sklearn":
assert (drf.predict(X) == drf_sk.predict(X)).all() == True
assert (drf.score(X, y) == drf_sk.score(X, y)).all() == True
assert (drf.decision_path(X)[1] == drf_sk.decision_path(X)[1]).all() == True
assert (drf.apply(X) == drf_sk.apply(X)).all() == True
print("Estimators")
print(drf.estimators_)
print(drf_sk.estimators_)
print("n_features")
print(drf.n_features_)
print(drf_sk.n_features_)
assert drf.n_features_ == drf_sk.n_features_
print("n_outputs")
print(drf.n_outputs_)
print(drf_sk.n_outputs_)
assert drf.n_outputs_ == drf_sk.n_outputs_
print("Feature importance")
print(drf.feature_importances_)
print(drf_sk.feature_importances_)
assert (drf.feature_importances_ == drf_sk.feature_importances_).all() == True
print("oob_score")
print(drf.oob_score_)
print(drf_sk.oob_score_)
assert drf.oob_score_ == drf_sk.oob_score_
print("oob_prediction")
print(drf.oob_prediction_)
print(drf_sk.oob_prediction_)
assert (drf.oob_prediction_ == drf_sk.oob_prediction_).all() == True
def predict_models(meta_m, m, meta_p, p):
print("Type m: ", m.dtype)
path_out = r"D:/UTwente/PycharmProjects/04_Risk_Model/data/skewed_leaves/prediction_nl_four_models_v3_20T_200S.csv"
Y = m[:, 0]
X = m[:, 1:]
n_esti = 20
spl = 200
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(X, Y, meta_m, train_size=0.60, random_state=0)
ensemble = RandomForestRegressor(n_estimators=n_esti, min_samples_leaf=spl, bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
print("Predicting with random forest")
pred_rf = ensemble.predict(p).reshape(-1, 1)
print("Prediction with the four models")
pred_sk = predicting_four_models_leaf_nodes_NL(ensemble, meta_p, p, pack).T
print(meta_p.shape, pred_sk.shape, pred_rf.shape)
stack = np.hstack((meta_p, pred_sk, pred_rf))
print("Stacked predictions: ", stack.shape, meta_p.shape)
# dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
# print(meta_m_test.shape, ytest.shape)
# stack = np.hstack((meta_m, Y.reshape(-1, 1)))
with open(path_out, "w", newline="") as w:
writer = csv.writer(w, delimiter=";")
for item in stack:
writer.writerow(item)
def _get_fitted_model(self, X, y):
model = RandomForestRegressor(
criterion=self.criterion,
n_estimators=self.n_estimators,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
bootstrap=self.bootstrap,
oob_score=self.oob_score,
n_jobs=self.n_jobs,
random_state=self.random_state,
verbose=self.verbose,
warm_start=self.warm_start,
ccp_alpha=self.ccp_alpha,
max_samples=self.max_samples)
self.model_ = model.fit(X, y)
self.train_leaf_indices_ = model.apply(X)
def test_varying_samples_per_node(meta_m, m):
print("Type m: ", m.dtype)
Y = m[:,0]
X = m[:,1:]
# Ynz, Xnz = trim_value(Y, X, 0)
xtrain, xtest, ytrain, ytest = train_test_split(X, Y, train_size=0.60, random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
for samples_per_leaf in range(500, 600, 100):
print("Samples per leaf node: ", samples_per_leaf)
ensemble = RandomForestRegressor(n_estimators=1, min_samples_leaf=samples_per_leaf, bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
print(leaves)
print(leaves.shape)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
pred_ = predicting_four_models_leaf_nodes(xtest, pack)
# dicens = ensemble_predictions_leaf_nodes(dicori)
break
def predict_one(self, X0):
'''
X0 must be a array of shape 1 x n_features
'''
assert X0.shape == (
1, self.n_features), "The shape of X0 should be 1 x n_features"
predict_one_leaf_indices = RandomForestRegressor.apply(
self, X0) ## 1 x n_estimators
leaf_equal_bool = np.equal(
self.leaf_indices,
predict_one_leaf_indices) ## n_sample x n_estimator
leaf_count = np.sum(leaf_equal_bool,
axis=0).reshape(1, -1) ## 1 x n_estimators
alpha_weights = 1 / self.B * np.sum(
leaf_equal_bool.astype(float) / leaf_count,
axis=1) ## n_sample x 1
# print(leaf_equal_bool.shape, leaf_equal_bool)
# print(leaf_count.shape, leaf_count)
# print(alpha_weights, np.sum(alpha_weights))
# print(alpha_weights.shape, np.sum(alpha_weights))
assert abs(np.sum(alpha_weights) -
1) < 0.01, "alpha weights calculation is wrong"
## A diagonal matrix
A = np.diag(alpha_weights) ## n_sample x n_sample
J_1d = np.ones((self.n_features + 1, 1))
J_1d[0] = 0
J = np.diag(J_1d) ## n_features + 1 x n_features + 1
delta_m = np.ones((self.n_samples,
self.n_features + 1)) ## n_sample x n_features + 1
delta_m[:, 1:] = self.train_x - X0
local_mu_theta = np.linalg.inv(delta_m.T @ A @ delta_m + self.lam *
J) @ delta_m.T @ A @ self.train_y
mu = local_mu_theta[0]
theta = local_mu_theta[1:]
return mu, theta
X1, X2 = np.meshgrid(x1, x2)
R1 = X1 - X2
R2 = X1 + X2
Z = 20 * np.maximum.reduce([np.exp(-2 * R1 ** 2),
np.exp(-1 * R2 ** 2),
2 * np.exp(-0.5 * (X1 ** 2 + X2 ** 2))])
fig, axes = plt.subplots(ncols=4, figsize=(18, 6))
for ax in axes.flat:
ax.set_aspect('equal', 'box')
ax.set_xlim(-3, 3)
ax.set_ylim(-3, 3)
rf_kernel = 1 - pairwise_distances(
forest.apply([[-1.5, 1.5]]), forest.apply(X), metric='hamming')
rf_kernel = rf_kernel.ravel() / rf_kernel.ravel().sum()
axes[0].imshow(
Z, extent=[-3, 3, -3, 3], origin='lower', cmap='YlGnBu_r', alpha=0.5)
axes[0].contour(X1, X2, Z, levels=n_contours,
linewidths=0.5, colors='k', linestyles='--')
axes[0].scatter(X[:, 0], X[:, 1],
edgecolor='k',
color='white',
sizes=50 * np.sqrt(rf_kernel))
axes[0].scatter(-1.5, 1.5, color='tomato', edgecolor='black', marker='P', s=50)
axes[0].set_title("Random Forest", fontsize=fontsize)
rf_kernel = 1 - pairwise_distances(
forest.apply([[0.5, -0.5]]), forest.apply(X), metric='hamming')
def test_varying_samples_per_node(meta_m, m):
print("Type m: ", m.dtype)
Y = m[:, 0]
X = m[:, 1:]
Ynz, Xnz, meta_m_nz = trim_value(Y, X, meta_m, 0)
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
# n_estimators = [1, 5, 10, 50, 100]
# samples_per_leaf = range(100, 1600, 100)
n_estimators = [10]
samples_per_leaf = [200, 400, 600, 1000, 1200]
start_all = time.time()
fig, ax = plt.subplots(nrows=5, ncols=5)
nrow = 0
for spl in samples_per_leaf:
start_it = time.time()
for n_esti in n_estimators:
print()
print("Analysis: RF with Skewed Leaves")
print("Samples per leaf node: ", spl)
print("Number of estimators: ", n_esti)
print("-" * 50)
ensemble = RandomForestRegressor(n_estimators=n_esti,
min_samples_leaf=spl,
bootstrap=True)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
pred_rf = ensemble.predict(xtest)
pred_sk = testing_four_models_leaf_nodes_v2(
ensemble, spl, n_esti, ytest, xtest, pack, pred_rf,
meta_m_test).T
print("This is pred sk: ", pred_sk.shape)
# stack = np.hstack((meta_m_test, pred))
# dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
write_proportion_of_zeros(spl, n_esti)
# plt.subplot(2, 2, 1)
# plt.hist(pred_sk[:, 0], bins=20)
# plt.subplot(2, 2, 2)
# plt.hist(pred_sk[:, 1], bins=20)
# plt.subplot(2, 2, 3)
# plt.hist(pred_sk[:, 2], bins=20)
# plt.subplot(2, 2, 4)
# plt.hist(pred_sk[:, 3], bins=20)
# plt.show()
plot_compare_histograms(ax, nrow, spl, ytest, pred_sk, pred_rf)
nrow += 1
stop_it = time.time()
print("--- Iteration elapsed {0} minutes ---".format(
np.divide(stop_it - start_it, 60)))
plt.show()
end_all = time.time()
print("--- Full program elapsed {0} hours ---".format(
np.divide(end_all - start_all, 3600)))
estimators = regr.estimators_ # description of each tree
importance = regr.feature_importances_ # an array of the fractional importance of the each feature
num_features = regr.n_features_ # the number of features
num_outputs = regr.n_outputs_ # the number of outputs when the model is built
#oob_score = regr.oob_score_ # score the training dataset using an out-of-bag estimator, this computes the average of correct classifications
# basically the coefficent of determination of R**2 using 'unseen' data not used to build the model
#oob_predict = regr.oob_prediction_ # The prediction for the values of training dataset using the oob method
# now having a look at the methods
leaf_indices = regr.apply(
x_test
) # get the numbers of the all the leaves the test dataset ends up in
decision_path = regr.decision_path(x_test)
parameters = regr.get_params() # the parameters of the model
predicted_age_array = regr.predict(
x_test
) # running the test dataset through the model, giving an array of predicted values
r_2_train = regr.score(
x_train, y_train) # calculating the R squared of the train dataset
r_2_test = regr.score(x_test,
y_test) # calculating the R squared of the test dataset
class PDFRandomForestRegressor(BaseEstimator, RegressorMixin):
"""A normal random forest, except that it stores the final leaf positions and delay times for each row of the training set. It will also have a specialized scoring method."""
def __init__(self,delaymin,delaymax,**kwargs):
self.rforest = RandomForestRegressor(**kwargs)
self.delay_min = delaymin
self.delay_max = delaymax
self.delay_bin_indices = np.arange(self.delay_max-self.delay_min+1)
self.delay_bin_values = np.arange(self.delay_min,self.delay_max+1)
#For each random forest, a dictionary mapping node id numbers to numpy arrays is also stored. These numpy arrays contain a histogram of the number of training models which fell into that node and their delay times.
self.node_delay_pdfs = [{}]*self.rforest.n_estimators
def fit(self, X,y,compute_pdf = False):
y_fit = self.restrict_range(y)
self.rforest.fit(X,y_fit)
if compute_pdf == True:
#Get the node ids for the training set:
self.set_node_pdfs(X,y_fit)
return self
def set_node_pdfs(self,X,y):
y_fit = self.restrict_range(y)
#Map the y values onto indices for the arrays:
y_indices = self.map_y_vals(y_fit)
nodes = self.apply(X)
#For each tree, make a 2D array containing the full range of integer target values along one axis (first axis), and the unique nodes along the other. Now, when the regression predicts a set of nodes for a given set of inputs, the full delay time distribution can be extracted by taking a slice along the unique node axis
for i in range(nodes.shape[1]):
unique_nodes,idxes = np.unique(nodes[:,i],return_inverse=True)
unique_node_indices = np.arange(len(unique_nodes)+1)
node_dict = {unique_nodes[i]:unique_node_indices[i] for i in range(len(unique_node_indices)-1)}
node_indices = unique_node_indices[idxes]
pdf_arr,xedges,yedges = np.histogram2d(y_fit,node_indices,bins=[self.delay_bin_values,unique_node_indices])
#print 'testing',np.sum(pdf_arr)
self.node_delay_pdfs[i] = {'node_dict':node_dict,'pdf_arr':pdf_arr}
def restrict_range(self,y):
y_restrict = y.copy()
y_restrict[y < self.delay_min] = self.delay_min
y_restrict[y > self.delay_max-1] = self.delay_max-1
return y_restrict
def map_y_vals(self,y):
y_map = self.restrict_range(y)
y_indices = y_map-self.delay_min
return y_indices
def predict(self,X):
return self.rforest.predict(X)
#Instead of just the normal prediction, which I believe just gives the average value of everything in the leaf node, predict a set of quantiles:
def predict_percentiles(self,X,percentiles):
p_nodes = self.apply(X)
pdf_arr = self.get_node_pdfs(p_nodes)
#print np.sum(pdf_arr,axis=1)
sys.exit
cdf_arr = np.cumsum(pdf_arr,axis=1)
cdf_arr_frac = (cdf_arr.T/cdf_arr[:,-1].astype(np.float)).T
#print pdf_arr[0,:]
#print cdf_arr_frac[0,:]
#sys.exit(1)
#print "test",cdf_arr_frac.shape,len(percentiles)
percentile_yvals = np.zeros((cdf_arr_frac.shape[0],len(percentiles)),dtype=np.int)
for i,ptile in enumerate(percentiles):
temp_cdf_arr_frac = cdf_arr_frac.copy()#These steps ensure that the y value is taken as the first index where the cdf goes above the percentile
temp_cdf_arr_frac[temp_cdf_arr_frac < ptile/100.] = 1000
indices = np.argmin(temp_cdf_arr_frac-ptile/100.,axis=1)
#indices = np.argmin(np.abs(cdf_arr_frac-ptile/100.),axis=1)
#print indices[0]
percentile_yvals[:,i] = self.delay_bin_values[indices]
#print i,self.delay_bin_values[indices]
#print pdf_arr[0,:]
#print cdf_arr_frac[0,:]
#print percentile_yvals[0,:],percentile_yvals.shape
#sys.exit(1)
return percentile_yvals
def compute_percentiles(self,X,y):
y_fit = self.restrict_range(y)
y_indices = self.map_y_vals(y_fit).astype(np.int)
p_nodes = self.apply(X)
pdf_arr = self.get_node_pdfs(p_nodes)
cdf_arr = np.cumsum(pdf_arr,axis=1)
cdf_arr_frac = (cdf_arr.T/cdf_arr[:,-1].astype(np.float)).T
#print cdf_arr_frac[0,:]
#print self.delay_bin_values
#print cdf_arr_frac.shape,y_fit.shape,y_fit[0]
#print 'test',cdf_arr_frac.shape,y_fit.shape,y_indices.min(),y_indices.max(),self.delay_bin_values.shape
#Now just need to compute the percentiles for all the y_indices
cdf_at_y = cdf_arr_frac[np.arange(len(y_indices)),y_indices]
#print "debug",cdf_at_y[0]
return cdf_at_y
# print cdf_at_y[:10],cdf_at_y.shape
def get_node_pdfs(self,nodes):
pdf_arr = np.zeros((nodes.shape[0],len(self.delay_bin_values)-1),dtype=np.int)
#print nodes.shape,pdf_arr.shape
for i,node_info in enumerate(self.node_delay_pdfs):
#print i,node_info['node_dict']
node_ids = [node_info['node_dict'][node] for node in nodes[:,i]]
#print node_ids
#print node_info['pdf_arr'].shape
#print 'debug',node_info['pdf_arr'][:,node_ids[0]]
temp_arr = np.array([node_info['pdf_arr'][:,node_id] for node_id in node_ids],dtype=pdf_arr.dtype)
pdf_arr += temp_arr
#print 'temp',temp_arr[0,:]
#print ""
return pdf_arr
def apply(self,X):
return self.rforest.apply(X)
def score(self,X,y):
return self.rforest.score(X,y)
#Compute how good each predicted value is based on how far away it is from the median value in percentiles:
def score_percentiles(self,X,y):
#First, compute the medians:
y_med = self.predict_percentiles(X,[50]).ravel()
#print y_med.shape
percentiles = self.compute_percentiles(X,y)
med_percentiles = self.compute_percentiles(X,y_med)#Have to do this step to take into account the discrete nature of the y values.
#print med_percentiles[:10],percentiles[:10]
return 1.-np.sum((med_percentiles-percentiles)**2)/float(len(y))
def predict_models(meta_m, m, meta_p, p):
path_out_tmp = r"D:/PycharmProjects/IGM_PhD_Materials/data/P04/out/pred_csv/prediction_nl_four_models_{0}T_{1}S.csv"
Y = m[:, 0]
X = m[:, 1:]
n_estimators = [10, 20, 50]
samples_per_leaf = range(100, 900, 100)
# The complete experiment for this paper corresponds to:
# n_estimators = [10, 20, 50]
# samples_per_leaf = range(100, 900, 100)
#
# NOTE THAT:
# + The complete execution takes ~40h in a Inteli7-8700 CPU @ 3.20GHz, 6 Core(s), 12 Logical Processor(s) w/ 16GB of RAM
# + The paper only shows results for SPL = [100, 200, 400, 600, 800] due to space constraints
start_all = time.time()
for n_esti in n_estimators:
start_it = time.time()
for spl in samples_per_leaf:
path_out = path_out_tmp.format(n_esti, spl)
print("\nTraining ensemble: ({0} T, {1} SPL)".format(n_esti, spl))
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=0)
ensemble = RandomForestRegressor(n_estimators=n_esti,
min_samples_leaf=spl,
bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
print("\tPredicting with random forest")
pred_rf = ensemble.predict(p).reshape(-1, 1)
print("\tPrediction with the four models")
pred_sk = predicting_four_models_leaf_nodes_NL(
ensemble, meta_p, p, pack, n_esti).T
stack = np.hstack((meta_p, pred_sk, pred_rf))
# dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
print("\tWriting results in CSV file")
with open(path_out, "w", newline="") as w:
writer = csv.writer(w, delimiter=";")
for item in stack:
writer.writerow(item)
stop_it = time.time()
print("--- Iteration elapsed {0} minutes ---".format(
np.divide(stop_it - start_it, 60)))
print()
end_all = time.time()
print("--- Full program elapsed {0} hours ---".format(
np.divide(end_all - start_all, 3600)))
# In[42]: print prediction print bias + np.sum(contributions, axis=1) # In[43]: # the basic feature importance feature provided by sklearn fit1.feature_importances_ # In[44]: # treeinterpreter uses the apply function to retrieve the leave indicies with the help of which, # the tree path is retrieved rf.apply # In[47]: rf.apply(instances) # In[ ]:
def get_data_clustering(data_z,
data_p,
n_instruments,
n_critics=50,
cluster_type="kmeans",
num_trees=5,
min_cluster_size=50,
critic_type="Gaussian"):
"""Return the centers, precisions, and normalizers of a data cover.
"""
if cluster_type == "forest":
from sklearn.ensemble import RandomForestRegressor
dtree = RandomForestRegressor(n_estimators=num_trees,
max_leaf_nodes=n_critics,
min_samples_leaf=min_cluster_size)
dtree.fit(data_z, data_p)
cluster_labels = dtree.apply(data_z)
cluster_ids = [
np.unique(cluster_labels[:, c])
for c in range(cluster_labels.shape[1])
]
elif cluster_type == "kmeans":
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=n_critics).fit(data_z)
cluster_labels = kmeans.labels_.reshape(-1, 1)
cluster_ids = [np.unique(cluster_labels)]
elif cluster_type == "random_points":
center_ids = np.random.choice(np.arange(data_z.shape[0]),
size=n_critics,
replace=False)
cluster_labels = np.zeros((data_z.shape[0], n_critics))
cluster_ids = np.ones((n_critics, 1))
for it, center in enumerate(center_ids):
distances = np.linalg.norm(data_z - data_z[center], axis=1)
cluster_members = np.argsort(distances)[:min_cluster_size]
cluster_labels[cluster_members, it] = 1
else:
raise Exception("Unknown option {}".format(cluster_type))
if critic_type == "Gaussian":
# We put a symmetric gaussian encompassing
# all the data points of each cluster of each clustering
center_grid = []
precision_grid = []
normalizers = []
data_z = np.array(data_z)
for tree in range(cluster_labels.shape[1]):
for leaf in cluster_ids[tree]:
center = np.mean(
data_z[cluster_labels[:, tree].flatten() == leaf, :],
axis=0)
distance = np.linalg.norm(data_z - center,
axis=1) / data_z.shape[1]
precision = 1. / (np.sqrt(2) *
(np.sort(distance)[min_cluster_size]))
normalizer = (precision**n_instruments) * np.sum(
np.exp(-(precision * distance)**2)) / (np.power(
2. * np.pi, n_instruments / 2.))
normalizers.append(normalizer)
center_grid.append(center)
precision_grid.append(precision)
# The proposed normalizing constant results in too small function values
# which result in too small losses and respective lack of scaling
# when using the exp function for the weights update.
# The code is kept for future fixes but overwritten
# with the following command.
# TODO: Explore normalizers and normalization of f(x)
normalizers = np.ones(len(center_grid), dtype="float32")
normalizers = np.array(normalizers, dtype="float32")
center_grid = np.array(center_grid, dtype="float32")
precision_grid = np.array(precision_grid, dtype="float32")
normalizers = tf.constant(normalizers, name="normalizers")
center_grid = tf.constant(center_grid, name="centers")
precision_grid = tf.constant(precision_grid, name="precisions")
else:
raise NotImplementedError("Uniform functions not supported.")
return normalizers, precision_grid, center_grid
class PDFRandomForestRegressor(BaseEstimator, RegressorMixin):
"""A normal random forest, except that it stores the final leaf positions and delay times for each row of the training set. It will also have a specialized scoring method."""
def __init__(self, delaymin, delaymax, **kwargs):
self.rforest = RandomForestRegressor(**kwargs)
self.delay_min = delaymin
self.delay_max = delaymax
self.delay_bin_indices = np.arange(self.delay_max - self.delay_min + 1)
self.delay_bin_values = np.arange(self.delay_min, self.delay_max + 1)
#For each random forest, a dictionary mapping node id numbers to numpy arrays is also stored. These numpy arrays contain a histogram of the number of training models which fell into that node and their delay times.
self.node_delay_pdfs = [{}] * self.rforest.n_estimators
def fit(self, X, y, compute_pdf=False):
y_fit = self.restrict_range(y)
self.rforest.fit(X, y_fit)
if compute_pdf == True:
#Get the node ids for the training set:
self.set_node_pdfs(X, y_fit)
return self
def set_node_pdfs(self, X, y):
y_fit = self.restrict_range(y)
#Map the y values onto indices for the arrays:
y_indices = self.map_y_vals(y_fit)
nodes = self.apply(X)
#For each tree, make a 2D array containing the full range of integer target values along one axis (first axis), and the unique nodes along the other. Now, when the regression predicts a set of nodes for a given set of inputs, the full delay time distribution can be extracted by taking a slice along the unique node axis
for i in range(nodes.shape[1]):
unique_nodes, idxes = np.unique(nodes[:, i], return_inverse=True)
unique_node_indices = np.arange(len(unique_nodes) + 1)
node_dict = {
unique_nodes[i]: unique_node_indices[i]
for i in range(len(unique_node_indices) - 1)
}
node_indices = unique_node_indices[idxes]
pdf_arr, xedges, yedges = np.histogram2d(
y_fit,
node_indices,
bins=[self.delay_bin_values, unique_node_indices])
#print 'testing',np.sum(pdf_arr)
self.node_delay_pdfs[i] = {
'node_dict': node_dict,
'pdf_arr': pdf_arr
}
def restrict_range(self, y):
y_restrict = y.copy()
y_restrict[y < self.delay_min] = self.delay_min
y_restrict[y > self.delay_max - 1] = self.delay_max - 1
return y_restrict
def map_y_vals(self, y):
y_map = self.restrict_range(y)
y_indices = y_map - self.delay_min
return y_indices
def predict(self, X):
return self.rforest.predict(X)
#Instead of just the normal prediction, which I believe just gives the average value of everything in the leaf node, predict a set of quantiles:
def predict_percentiles(self, X, percentiles):
p_nodes = self.apply(X)
pdf_arr = self.get_node_pdfs(p_nodes)
#print np.sum(pdf_arr,axis=1)
sys.exit
cdf_arr = np.cumsum(pdf_arr, axis=1)
cdf_arr_frac = (cdf_arr.T / cdf_arr[:, -1].astype(np.float)).T
#print pdf_arr[0,:]
#print cdf_arr_frac[0,:]
#sys.exit(1)
#print "test",cdf_arr_frac.shape,len(percentiles)
percentile_yvals = np.zeros((cdf_arr_frac.shape[0], len(percentiles)),
dtype=np.int)
for i, ptile in enumerate(percentiles):
temp_cdf_arr_frac = cdf_arr_frac.copy(
) #These steps ensure that the y value is taken as the first index where the cdf goes above the percentile
temp_cdf_arr_frac[temp_cdf_arr_frac < ptile / 100.] = 1000
indices = np.argmin(temp_cdf_arr_frac - ptile / 100., axis=1)
#indices = np.argmin(np.abs(cdf_arr_frac-ptile/100.),axis=1)
#print indices[0]
percentile_yvals[:, i] = self.delay_bin_values[indices]
#print i,self.delay_bin_values[indices]
#print pdf_arr[0,:]
#print cdf_arr_frac[0,:]
#print percentile_yvals[0,:],percentile_yvals.shape
#sys.exit(1)
return percentile_yvals
def compute_percentiles(self, X, y):
y_fit = self.restrict_range(y)
y_indices = self.map_y_vals(y_fit).astype(np.int)
p_nodes = self.apply(X)
pdf_arr = self.get_node_pdfs(p_nodes)
cdf_arr = np.cumsum(pdf_arr, axis=1)
cdf_arr_frac = (cdf_arr.T / cdf_arr[:, -1].astype(np.float)).T
#print cdf_arr_frac[0,:]
#print self.delay_bin_values
#print cdf_arr_frac.shape,y_fit.shape,y_fit[0]
#print 'test',cdf_arr_frac.shape,y_fit.shape,y_indices.min(),y_indices.max(),self.delay_bin_values.shape
#Now just need to compute the percentiles for all the y_indices
cdf_at_y = cdf_arr_frac[np.arange(len(y_indices)), y_indices]
#print "debug",cdf_at_y[0]
return cdf_at_y
# print cdf_at_y[:10],cdf_at_y.shape
def get_node_pdfs(self, nodes):
pdf_arr = np.zeros((nodes.shape[0], len(self.delay_bin_values) - 1),
dtype=np.int)
#print nodes.shape,pdf_arr.shape
for i, node_info in enumerate(self.node_delay_pdfs):
#print i,node_info['node_dict']
node_ids = [node_info['node_dict'][node] for node in nodes[:, i]]
#print node_ids
#print node_info['pdf_arr'].shape
#print 'debug',node_info['pdf_arr'][:,node_ids[0]]
temp_arr = np.array(
[node_info['pdf_arr'][:, node_id] for node_id in node_ids],
dtype=pdf_arr.dtype)
pdf_arr += temp_arr
#print 'temp',temp_arr[0,:]
#print ""
return pdf_arr
def apply(self, X):
return self.rforest.apply(X)
def score(self, X, y):
return self.rforest.score(X, y)
#Compute how good each predicted value is based on how far away it is from the median value in percentiles:
def score_percentiles(self, X, y):
#First, compute the medians:
y_med = self.predict_percentiles(X, [50]).ravel()
#print y_med.shape
percentiles = self.compute_percentiles(X, y)
med_percentiles = self.compute_percentiles(
X, y_med
) #Have to do this step to take into account the discrete nature of the y values.
#print med_percentiles[:10],percentiles[:10]
return 1. - np.sum((med_percentiles - percentiles)**2) / float(len(y))
class QuantileRandomForestRegressor:
"""A quantile random forest regressor based on the scikit-learn RandomForestRegressor
A wrapper around the RandomForestRegressor which summarizes based on quantiles rather than
the mean. Note that quantile predicitons take much longer than mean predictions.
Parameters
----------
nthreads : int, default=1
number of threads to used
rf_kwargs : array or array like
kwargs to be passed to the RandomForestRegressor
See Also
--------
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html?highlight=randomforestregressor#sklearn.ensemble.RandomForestRegressor.apply
"""
def __init__(self, nthreads=1, **rf_kwargs):
rf_kwargs['n_jobs'] = nthreads
self.forest = RandomForestRegressor(**rf_kwargs)
set_num_threads(nthreads)
def fit(self, X, y, sample_weight=None):
"""
Build a forest of trees from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted
to ``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.
Returns
-------
self : object
"""
self.forest.fit(X, y, sample_weight)
self.trainy = y.copy()
self.trainX = X.copy()
def predict(self, X, qntl):
"""
Predict regression target for X.
The predicted regression target of an input sample is computed as the
quantile predicted regression targets of the trees in the forest.
Note: Not possible for multioutput regression.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
qntl : {array-like} of shape (n_quantiles)
Quantile or sequence of quantiles to compute, which must be between
0 and 1 inclusive. Passed to numpy.quantile.
Returns
-------
y : ndarray of shape (n_samples, n_quantiles)
The predicted values.
"""
if len(self.trainy.shape)>1:
raise RuntimeError("Quantile prediction is not possible with multioutput regression.")
qntl = np.asanyarray(qntl)
ntrees = self.forest.n_estimators
ntrain = self.trainy.shape[0]
train_tree_node_ID = np.zeros([ntrain, ntrees])
npred = X.shape[0]
pred_tree_node_ID = np.zeros([npred, ntrees])
for i in range(ntrees):
train_tree_node_ID[:, i] = self.forest.estimators_[i].apply(self.trainX)
pred_tree_node_ID[:, i] = self.forest.estimators_[i].apply(X)
ypred_pcts = find_quant(self.trainy, train_tree_node_ID,
pred_tree_node_ID, qntl)
return ypred_pcts
def predict_sample(self, X, n_draws):
"""
Predict regression target for X.
The predicted regression target of an input sample is computed as a
random sample of the predicted regression targets of the trees in the forest.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
n_sample : {int}
number of sample to draw from the predicted regression targets
Returns
-------
y : ndarray of shape (n_samples, n_draws) or (n_samples, n_outputs, n_draws)
The predicted values.
"""
ntrees = self.forest.n_estimators
ntrain = self.trainy.shape[0]
train_tree_node_ID = np.zeros([ntrain, ntrees])
npred = X.shape[0]
pred_tree_node_ID = np.zeros([npred, ntrees])
for i in range(ntrees):
train_tree_node_ID[:, i] = self.forest.estimators_[i].apply(self.trainX)
pred_tree_node_ID[:, i] = self.forest.estimators_[i].apply(X)
ypred_draws = find_sample(self.trainy, train_tree_node_ID,
pred_tree_node_ID, n_draws)
return ypred_draws
def apply(self, X):
"""
wrapper for sklearn.ensemble.RandomForestRegressor.apply
Apply trees in the forest to X, return leaf indices.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
X_leaves : ndarray of shape (n_samples, n_estimators)
For each datapoint x in X and for each tree in the forest,
return the index of the leaf x ends up in.
"""
return self.forest.apply(X)
def decision_path(self, X):
"""
wrapper for sklearn.ensemble.RandomForestRegressor.decision_path
Return the decision path in the forest.
.. versionadded:: 0.18
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
indicator : sparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates
that the samples goes through the nodes. The matrix is of CSR
format.
n_nodes_ptr : ndarray of shape (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
gives the indicator value for the i-th estimator.
"""
return self.forest.decision_path(X)
def set_params(self, **params):
"""
wrapper for sklearn.ensemble.RandomForestRegressor.set_params
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : object
Estimator instance.
"""
return self.forestset_params(**params)
print "Instance", i
print "Bias (trainset mean)", bias[i]
print "Feature contributions:"
for c, feature in sorted(zip(contributions[i], boston.feature_names),
key=lambda x: -abs(x[0])):
print feature, round(c, 2)
print "-" * 20
# In[42]:
print prediction
print bias + np.sum(contributions, axis=1)
# In[43]:
# the basic feature importance feature provided by sklearn
fit1.feature_importances_
# In[44]:
# treeinterpreter uses the apply function to retrieve the leave indicies with the help of which,
# the tree path is retrieved
rf.apply
# In[47]:
rf.apply(instances)
# In[ ]:
def test_varying_samples_per_node(meta_m, m):
print("Type m: ", m.dtype)
path_out = r"/home/irene/PycharmProjects/04_Risk_Model/data/poisson_leaves/prediction_ytest.csv"
Y = m[:, 0]
X = m[:, 1:]
# Ynz, Xnz = trim_value(Y, X, 0)
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
nrow = 0
nrow2 = 0
fig, ax = plt.subplots(nrows=5, ncols=5)
fig2, ax2 = plt.subplots(nrows=1, ncols=5, figsize=(20, 8))
tit1 = "Effect of the number of samples per leaf node on the predicted distributions (n_esti=5)"
tit2 = "Predicted distributions from above in function of the number of samples per leaf node"
plt.suptitle(tit2, size=20)
mean_ytest = np.mean(ytest)
for samples_per_leaf in range(500, 1400, 200):
print("Samples per leaf node: ", samples_per_leaf)
ensemble = RandomForestRegressor(n_estimators=1,
min_samples_leaf=samples_per_leaf,
bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
pred = predicting_four_models_leaf_nodes(ytest, xtest, pack).T
print(pred.shape, meta_m_test.shape)
stack = np.hstack((meta_m_test, pred))
print("Shape of stack: ", stack.shape)
dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
header = "rowid;longitude;latitude;predpoi;prednb;predzip;predzinb"
fmts = ["%d", "%d", "%d", "%.4f", "%.4f", "%.4f", "%.4f"]
# np.savetxt(path_out, stack, delimiter=";", fmt=fmts, header=header)
pred_rf = ensemble.predict(xtest)
rmse_rf = np.sqrt(mean_squared_error(ytest, pred_rf))
print()
print("RMSE RF: ", rmse_rf)
# ax = plot_compare_histograms(ax, nrow, samples_per_leaf, mean_ytest, ytest, pred, pred_rf)
print(pred.shape, pred_rf.reshape(-1, 1).shape)
allpreds = np.hstack((pred, pred_rf.reshape(-1, 1)))
labels = ['Poisson', 'NB', 'ZIP', "ZINB", "RF-Classic"]
labelsize = 16
rcParams['xtick.labelsize'] = labelsize
rcParams['ytick.labelsize'] = labelsize
ax2[nrow2].set_title("SPL: {0}".format(samples_per_leaf))
ax2[nrow2].set_facecolor('#F5F5F5')
box = ax2[nrow2].boxplot(allpreds, patch_artist=True)
# ax2[nrow2].xaxis.set_ticks(labels)
ax2[nrow2].set_xticklabels(labels,
fontsize=16,
fontdict={'fontsize': 16})
colors = ['#A87128', '#004561', '#3C5B43', '#85243C', '#615048']
for patch, color in zip(box['boxes'], colors):
patch.set_facecolor(color)
ax2[nrow2].yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=0.5)
nrow += 1
nrow2 += 1
plt.show()
test.append(test_data[i])
test_y.append(float(y[i]))
else:
data.append(test_data[i])
i += 1
test1_y = np.asarray(test_y, dtype=np.float32)
#test = np.asarray(test, dtype=np.float32)
#test1_y = test1_y.transpose
#print(test_y)
#print data
for i1 in range(0,10):
forest = RandomForestRegressor(n_estimators = 100, max_depth = 3)
#print("--- %s seconds ---" % (time.clock() - start_time))
forest = forest.fit(test,test1_y)
out1 = forest.apply(test)
out = forest.score(test,test1_y)
print out
print out1
#print("--- %s seconds ---" % (time.clock() - start_time))
output = forest.predict(data)
i = 0
error = 0
error1 = 0
while i < len(output):
if abs(output[i] - y[test_len+i]) > 0.01:
#print(i)
#print(y[test_len+i])
#print(output[i])
error += abs(output[i] - y[test_len+i])
error1 += 1
random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Trim data: ", Ynz.shape, Xnz.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
ensemble = RandomForestRegressor(n_estimators=100,
min_samples_leaf=500,
bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
dicpoi = poisson_predictions_leaf_nodes(dicori)
dicens = ensemble_predictions_leaf_nodes(dicori)
ypred_poi = np.mean(poisson_predictions_testing(dicori, dicpoi, xtest), axis=1)
ypred_ens = ensemble_predictions_testing(ensemble, xtest)
plot_poisson_ensemble_raw(ypred_poi, ypred_ens, Yavg, ytest)
# rmse = np.sqrt(mean_squared_error(ytest, ypred_poi))
#
feature[i],
threshold[i],
children_right[i],
))
print()
# First let's retrieve the decision path of each sample. The decision_path
# method allows to retrieve the node indicator functions. A non zero element of
# indicator matrix at the position (i, j) indicates that the sample i goes
# through the node j.
node_indicator = estimator.decision_path(X_test)
# Similarly, we can also have the leaves ids reached by each sample.
leave_id = estimator.apply(X_test)
# Now, it's possible to get the tests that were used to predict a sample or
# a group of samples. First, let's make it for the sample.
sample_id = 0
node_index = node_indicator.indices[node_indicator.indptr[sample_id]:
node_indicator.indptr[sample_id + 1]]
print('Rules used to predict sample %s: ' % sample_id)
for node_id in node_index:
if leave_id[sample_id] == node_id:
continue
if X_test[sample_id, feature[node_id]] <= threshold[node_id]:
threshold_sign = "<="
def __init__(self,
X_train,
MR_train,
X_val,
MR_val,
fe_type="rf",
fe=None,
n_estimators=200,
max_features=0.5,
min_samples_leaf=10,
regularization=0.001):
# Features and the target model response
self.X_train = X_train
self.MR_train = MR_train
self.X_val = X_val
self.MR_val = MR_val
# Forest Ensemble Parameters
self.n_estimators = n_estimators
self.max_features = max_features
self.min_samples_leaf = min_samples_leaf
# Local Linear Model Parameters
self.regularization = regularization
# Data parameters
num_features = X_train.shape[1]
self.num_features = num_features
num_train = X_train.shape[0]
self.num_train = num_train
num_val = X_val.shape[0]
# Fit a Forest Ensemble to the model response
if fe is None:
if fe_type == "rf":
fe = RandomForestRegressor(n_estimators=n_estimators,
min_samples_leaf=min_samples_leaf,
max_features=max_features)
elif fe_type == "gbrt":
fe = GradientBoostingRegressor(
n_estimators=n_estimators,
min_samples_leaf=min_samples_leaf,
max_features=max_features,
max_depth=None)
else:
print("Unknown FE type ", fe)
import sys
sys.exit(0)
fe.fit(X_train, MR_train)
else:
self.n_estimators = n_estimators = len(fe.estimators_)
self.fe = fe
train_leaf_ids = fe.apply(X_train)
self.train_leaf_ids = train_leaf_ids
val_leaf_ids_list = fe.apply(X_val)
# Compute the feature importances: Non-normalized @ Root
scores = np.zeros(num_features)
if fe_type == "rf":
for i in range(n_estimators):
splits = fe[
i].tree_.feature #-2 indicates leaf, index 0 is root
if splits[0] != -2:
scores[splits[0]] += fe[i].tree_.impurity[
0] #impurity reduction not normalized per tree
elif fe_type == "gbrt":
for i in range(n_estimators):
splits = fe[
i, 0].tree_.feature #-2 indicates leaf, index 0 is root
if splits[0] != -2:
scores[splits[0]] += fe[i, 0].tree_.impurity[
0] #impurity reduction not normalized per tree
self.feature_scores = scores
mostImpFeats = np.argsort(-scores)
# Find the number of features to use for MAPLE
retain_best = 0
rmse_best = np.inf
for retain in range(1, num_features + 1):
# Drop less important features for local regression
X_train_p = np.delete(X_train, mostImpFeats[retain:], axis=1)
X_val_p = np.delete(X_val, mostImpFeats[retain:], axis=1)
lr_predictions = np.empty([num_val], dtype=float)
for i in range(num_val):
weights = self.training_point_weights(val_leaf_ids_list[i])
# Local linear model
lr_model = Ridge(alpha=regularization)
lr_model.fit(X_train_p, MR_train, weights)
lr_predictions[i] = lr_model.predict(X_val_p[i].reshape(1, -1))
rmse_curr = np.sqrt(mean_squared_error(lr_predictions, MR_val))
if rmse_curr < rmse_best:
rmse_best = rmse_curr
retain_best = retain
self.retain = retain_best
self.X = np.delete(X_train, mostImpFeats[retain_best:], axis=1)
def test_varying_samples_per_node(meta_m, m):
path_out = r"/home/irene/PycharmProjects/04_Risk_Model/data/skewed_leaves/to_keep_for_taylor/remove_testing_SPL{0}_NESTI{1}.csv"
print("Type m: ", m.dtype)
Y = m[:, 0]
X = m[:, 1:]
Ynz, Xnz, meta_m_nz = trim_value(Y, X, meta_m, 0)
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
n_estimators = [5]
samples_per_leaf = [1000]
start_all = time.time()
fig, ax = plt.subplots(nrows=5, ncols=5)
nrow = 0
for spl in samples_per_leaf:
start_it = time.time()
for n_esti in n_estimators:
print()
print("Analysis: RF with Skewed Leaves")
print("Samples per leaf node: ", spl)
print("Number of estimators: ", n_esti)
print("-" * 50)
ensemble = RandomForestRegressor(n_estimators=n_esti,
min_samples_leaf=spl,
bootstrap=True)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
for feature in leaves.T:
nnodes = str(len(np.unique(feature))) + "\n"
f.write(nnodes)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
pred_rf = ensemble.predict(xtest)
pred_sk = testing_four_models_leaf_nodes_v2(
ensemble, spl, n_esti, ytest, xtest, pack, pred_rf,
meta_m_test).T
print("Now saving")
print(meta_m_test.shape, ytest.shape, pred_sk.shape, pred_rf.shape)
stack = np.hstack((meta_m_test, ytest.reshape(-1, 1), pred_sk,
pred_rf.reshape(-1, 1)))
# dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
write_proportion_of_zeros(spl, n_esti)
# with open(path_out.format(spl, n_esti), "w", newline="") as w:
# writer = csv.writer(w, delimiter=";")
# for item in stack:
# writer.writerow(item)
plot_compare_histograms(ax, nrow, spl, ytest, pred_sk, pred_rf)
nrow += 1
stop_it = time.time()
print("--- Iteration elapsed {0} minutes ---".format(
np.divide(stop_it - start_it, 60)))
plt.show()
end_all = time.time()
print("--- Full program elapsed {0} hours ---".format(
np.divide(end_all - start_all, 3600)))
class _LinearForest(BaseEstimator):
"""Base class for Linear Forest meta-estimator.
Warning: This class should not be used directly. Use derived classes
instead.
"""
def __init__(self, base_estimator, *, n_estimators, max_depth,
min_samples_split, min_samples_leaf, min_weight_fraction_leaf,
max_features, max_leaf_nodes, min_impurity_decrease,
bootstrap, oob_score, n_jobs, random_state, ccp_alpha,
max_samples):
self.base_estimator = base_estimator
self.n_estimators = n_estimators
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.bootstrap = bootstrap
self.oob_score = oob_score
self.n_jobs = n_jobs
self.random_state = random_state
self.ccp_alpha = ccp_alpha
self.max_samples = max_samples
def _sigmoid(self, y):
"""Expit function (a.k.a. logistic sigmoid).
Parameters
----------
y : array-like of shape (n_samples, )
The array to apply expit to element-wise.
Returns
-------
y : array-like of shape (n_samples, )
Expits.
"""
return np.exp(y) / (1 + np.exp(y))
def _inv_sigmoid(self, y):
"""Logit function.
Parameters
----------
y : array-like of shape (n_samples, )
The array to apply logit to element-wise.
Returns
-------
y : array-like of shape (n_samples, )
Logits.
"""
y = y.clip(1e-3, 1 - 1e-3)
return np.log(y / (1 - y))
def _fit(self, X, y, sample_weight=None):
"""Build a Linear Boosting from the training set (X, y).
Parameters
----------
X : array-like of shape (n_samples, n_features)
The training input samples.
y : array-like of shape (n_samples, ) or also (n_samples, n_targets) for
multitarget regression.
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape (n_samples, ), default=None
Sample weights.
Returns
-------
self : object
"""
if not hasattr(self.base_estimator, "fit_intercept"):
raise ValueError(
"Only linear models are accepted as base_estimator. "
"Select one from linear_model class of scikit-learn.")
if not is_regressor(self.base_estimator):
raise ValueError(
"Select a regressor linear model as base_estimator.")
n_sample, self.n_features_in_ = X.shape
if hasattr(self, "classes_"):
class_to_int = dict(map(reversed, enumerate(self.classes_)))
y = np.array([class_to_int[i] for i in y])
y = self._inv_sigmoid(y)
self.base_estimator_ = deepcopy(self.base_estimator)
self.base_estimator_.fit(X, y, sample_weight)
resid = y - self.base_estimator_.predict(X)
criterion = "squared_error" if _sklearn_v1 else "mse"
self.forest_estimator_ = RandomForestRegressor(
n_estimators=self.n_estimators,
criterion=criterion,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
bootstrap=self.bootstrap,
oob_score=self.oob_score,
n_jobs=self.n_jobs,
random_state=self.random_state,
ccp_alpha=self.ccp_alpha,
max_samples=self.max_samples,
)
self.forest_estimator_.fit(X, resid, sample_weight)
if hasattr(self.base_estimator_, "coef_"):
self.coef_ = self.base_estimator_.coef_
if hasattr(self.base_estimator_, "intercept_"):
self.intercept_ = self.base_estimator_.intercept_
self.feature_importances_ = self.forest_estimator_.feature_importances_
return self
def apply(self, X):
"""Apply trees in the forest to X, return leaf indices.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input samples.
Returns
-------
X_leaves : ndarray of shape (n_samples, n_estimators)
For each datapoint x in X and for each tree in the forest,
return the index of the leaf x ends up in.
"""
check_is_fitted(self, attributes="base_estimator_")
return self.forest_estimator_.apply(X)
def decision_path(self, X):
"""Return the decision path in the forest.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input samples.
Returns
-------
indicator : sparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates
that the samples goes through the nodes. The matrix is of CSR
format.
n_nodes_ptr : ndarray of shape (n_estimators + 1, )
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
gives the indicator value for the i-th estimator.
"""
check_is_fitted(self, attributes="base_estimator_")
return self.forest_estimator_.decision_path(X)
def test_RandomForest():
X1 = np.arange(0, 10, 0.1)
X2 = np.arange(10, 20, 0.1)
y = np.sin(X1).ravel() + np.cos(X2).ravel()
X_df = pd.DataFrame(np.array([X1, X2]).T, columns=['x1', 'x2'])
rf_regr = RandomForestRegressor(n_estimators=1000,
max_depth=5,
bootstrap=False)
rf_regr.fit(X_df, y)
with StopWatch("LucidEnsemble Random Forest construction"):
lucid_rf = make_LucidEnsemble(rf_regr,
feature_names=X_df.columns,
print_precision=5)
# If this is not float32 there are precision errors
# apparently DecisionTreeRegressor within RandomForestRegressor
# requires that the matrix be of type float32 so there is a
# type conversion from types to float32
X_df = X_df.astype(np.float32)
with StopWatch("Scikit-learn Random Forest prediction"):
rf_pred = rf_regr.predict(X_df)
with StopWatch("Lucid Random Forest (non-compressed) prediction"):
lucid_rf_pred = lucid_rf.predict(X_df)
######################################################
# test prediction outputted from LucidEnsemble
np.testing.assert_almost_equal(lucid_rf_pred, rf_pred)
assert (np.all(rf_regr.apply(X_df) == lucid_rf.apply(X_df)))
with StopWatch("Compression of Lucid Random Forest"):
compressed_lucid_rf = lucid_rf.compress()
print("{} unique nodes and {} # of estimators".format(
compressed_lucid_rf.n_leaves, len(lucid_rf)))
with StopWatch("Lucid Random Forest (compressed) prediction"):
crf_pred = compressed_lucid_rf.predict(X_df)
np.testing.assert_almost_equal(crf_pred, rf_pred)
######################################################
# test comparison, compare the leaves of two
# LucidEnsembles made from the the same arguments
lucid_rf2 = make_LucidEnsemble(rf_regr,
feature_names=X_df.columns,
print_precision=3)
compressed_lucid_rf2 = lucid_rf2.compress()
assert (set(compressed_lucid_rf.leaves) == set(
compressed_lucid_rf2.leaves))
script_dir = os.path.dirname(__name__)
######################################################
# test pickling functionality
pickle_path = os.path.join(script_dir, 'lucid_rf.pkl')
with open(pickle_path, 'wb') as fh:
pickle.dump(lucid_rf, fh)
with open(pickle_path, 'rb') as fh:
lucid_rf_pickle = pickle.load(fh)
np.testing.assert_almost_equal(lucid_rf_pickle.predict(X_df),
lucid_rf_pred)
os.remove(pickle_path)
pickle_path = os.path.join(script_dir, 'compressed_lucid_rf.pkl')
with open(pickle_path, 'wb') as fh:
pickle.dump(compressed_lucid_rf, fh)
with open(pickle_path, 'rb') as fh:
compressed_lucid_rf_pickle = pickle.load(fh)
np.testing.assert_almost_equal(
compressed_lucid_rf_pickle.predict(X_df), crf_pred)
os.remove(pickle_path)
def test_varying_samples_per_node(meta_m, m):
hs = []
path_out = r"/home/irene/PycharmProjects/04_Risk_Model/data/skewed_leaves/explore_trees/testing_SPL{0}_NESTI{1}.csv"
print("Type m: ", m.dtype)
Y = m[:, 0]
X = m[:, 1:]
Ynz, Xnz, meta_m_nz = trim_value(Y, X, meta_m, 0)
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
nt = 10
n_estimators = [nt]
samples_per_leaf = [100]
start_all = time.time()
fig, ax = plt.subplots(nrows=6, ncols=5, sharex=False, sharey=False)
plt.subplots_adjust(wspace=0.5, hspace=0.5)
nrow = 0
for spl in samples_per_leaf:
start_it = time.time()
for n_esti in n_estimators:
print()
print("Analysis: RF with Skewed Leaves")
print("Samples per leaf node: ", spl)
print("Number of estimators: ", n_esti)
print("-" * 50)
ensemble = RandomForestRegressor(n_estimators=n_esti,
min_samples_leaf=spl,
bootstrap=True)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
pred_rf = ensemble.predict(xtest)
pred_sk = testing_four_models_leaf_nodes_v2(
ensemble, spl, n_esti, ytest, xtest, pack, pred_rf,
meta_m_test).T
stack = np.hstack((meta_m_test, ytest.reshape(-1, 1), pred_sk,
pred_rf.reshape(-1, 1)))
save_tree_graph(ensemble, nt)
# dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
# write_proportion_of_zeros(spl, n_esti)
# with open(path_out.format(spl, n_esti), "w", newline="") as w:
# writer = csv.writer(w, delimiter=";")
# for item in stack:
# writer.writerow(item)
# h = plot_compare_histograms(ax, nrow, spl, ytest, pred_sk, pred_rf)
# hs.append(h)
# plot_pred_vs_true(ax, nrow, spl, ytest, pred_sk, pred_rf)
nrow += 1
stop_it = time.time()
print("--- Iteration elapsed {0} minutes ---".format(
np.divide(stop_it - start_it, 60)))
# fig.legend(hs, loc="center right", borderaxespad=0.1, title="Legend Title
# path_fig_out = r"/home/irene/Pictures/0403_Compare_Histograms_full05.png"
# manager = plt.get_current_fig_manager()
# manager.window.showMaximized()
# plt.pause(10)
# plt.gcf().savefig(path_fig_out, format='png', dpi=300)
end_all = time.time()
print("--- Full program elapsed {0} hours ---".format(
np.divide(end_all - start_all, 3600)))
def test_varying_samples_per_node(meta_m, m):
print("Type m: ", m.dtype)
path_out = r"/home/irene/PycharmProjects/04_Risk_Model/data/poisson_leaves/prediction_ytest.csv"
Y = m[:, 0]
X = m[:, 1:]
# Ynz, Xnz = trim_value(Y, X, 0)
xtrain, xtest, ytrain, ytest, meta_m_train, meta_m_test = train_test_split(
X, Y, meta_m, train_size=0.60, random_state=0)
print('Type xtrain: ', xtrain.dtype)
print("Raw data: ", Y.shape, X.shape)
print("Train data", ytrain.shape, xtrain.shape)
print("Test data: ", ytest.shape, xtest.shape)
for samples_per_leaf in range(500, 600, 100):
print("Samples per leaf node: ", samples_per_leaf)
ensemble = RandomForestRegressor(n_estimators=1,
min_samples_leaf=1000,
bootstrap=False)
ensemble.fit(xtrain, ytrain)
leaves = ensemble.apply(xtrain)
dicori = samples_per_leaf_node(leaves, xtrain, ytrain)
pack = fitting_four_models_leaf_nodes(dicori)
pred = predicting_four_models_leaf_nodes(ytest, xtest, pack).T
print(pred.shape, meta_m_test.shape)
stack = np.hstack((meta_m_test, pred))
print("Shape of stack: ", stack.shape)
dicens = ensemble_predictions_leaf_nodes(ensemble, dicori)
header = "rowid;longitude;latitude;predpoi;prednb;predzip;predzinb"
fmts = ["%d", "%d", "%d", "%.4f", "%.4f", "%.4f", "%.4f"]
# np.savetxt(path_out, stack, delimiter=";", fmt=fmts, header=header)
pred_rf = ensemble.predict(xtest)
rmse_rf = np.sqrt(mean_squared_error(ytest, pred_rf))
print()
print("RMSE RF: ", rmse_rf)
plt.subplot(2, 3, 1)
plt.hist(pred[:, 0], bins=50)
plt.hist(ytest, bins=50)
plt.subplot(2, 3, 2)
plt.hist(pred[:, 1], bins=50)
plt.hist(ytest, bins=50)
plt.subplot(2, 3, 3)
plt.hist(pred[:, 2], bins=50)
plt.hist(ytest, bins=50)
plt.subplot(2, 3, 4)
plt.hist(pred[:, 3], bins=50)
plt.hist(ytest, bins=50)
plt.subplot(2, 3, 5)
plt.hist(ytest, bins=50)
plt.hist(pred_rf)
plt.show()
break