Doctorado-ML/stree
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First approach to Platt scaling

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Ricardo Montañana Gómez
2020-05-18 11:51:27 +02:00
parent 86a9ef2f3a
commit e52cbbb192
3 changed files with 114 additions and 50 deletions
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65
trees/Stree.py
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@@ -52,6 +52,7 @@ class Stree(BaseEstimator, ClassifierMixin):
if self.__use_predictions:
yp = node._clf.predict(data)
down = (yp == 1).reshape(-1, 1)
res = node._clf.decision_function(data)
else:
# doesn't work with multiclass as each sample has to do inner product with its own coeficients
# computes positition of every sample is w.r.t. the hyperplane
@@ -60,9 +61,15 @@ class Stree(BaseEstimator, ClassifierMixin):
up = ~down
data_down = data[down[:, 0]] if any(down) else None
indices_down = indices[down[:, 0]] if any(down) else None
res_down = res[down[:, 0]] if any(down) else None
data_up = data[up[:, 0]] if any(up) else None
indices_up = indices[up[:, 0]] if any(up) else None
return [data_down, indices_down, data_up, indices_up]
res_up = res[up[:, 0]] if any(up) else None
#if any(up):
# print("+++++up", data_up.shape, indices_up.shape, res_up.shape)
#if any(down):
# print("+++++down", data_down.shape, indices_down.shape, res_down.shape )
return [data_up, indices_up, data_down, indices_down, res_up, res_down]
def fit(self, X: np.ndarray, y: np.ndarray, title: str = 'root') -> 'Stree':
X, y = check_X_y(X, y.ravel())
@@ -92,7 +99,7 @@ class Stree(BaseEstimator, ClassifierMixin):
random_state=self._random_state)
clf.fit(X, y)
tree = Snode(clf, X, y, title)
X_U, y_u, X_D, y_d = self._split_data(tree, X, y)
X_U, y_u, X_D, y_d, _, _ = self._split_data(tree, X, y)
if X_U is None or X_D is None:
# didn't part anything
return Snode(clf, X, y, title + ', <cgaf>')
@@ -100,20 +107,22 @@ class Stree(BaseEstimator, ClassifierMixin):
tree.set_down(self.train(X_D, y_d, title + ' - Down'))
return tree
def _predict_values(self, X: np.array) -> np.array:
def _reorder_results(self, y: np.array, indices: np.array) -> np.array:
y_ordered = np.zeros(y.shape, dtype=int if y.ndim == 1 else float)
indices = indices.astype(int)
for i, index in enumerate(indices):
y_ordered[index] = y[i]
return y_ordered
def predict(self, X: np.array) -> np.array:
def predict_class(xp: np.array, indices: np.array, node: Snode) -> np.array:
if xp is None:
return [], []
if node.is_leaf():
# set a class for every sample in dataset
prediction = np.full((xp.shape[0], 1), node._class)
if self.__proba:
prediction_proba = np.full((xp.shape[0], 1), node._belief)
#prediction_proba = self._linear_function(xp, node)
return np.append(prediction, prediction_proba, axis=1), indices
else:
return prediction, indices
u, i_u, d, i_d = self._split_data(node, xp, indices)
return prediction, indices
u, i_u, d, i_d, _, _ = self._split_data(node, xp, indices)
k, l = predict_class(d, i_d, node.get_down())
m, n = predict_class(u, i_u, node.get_up())
return np.append(k, m), np.append(l, n)
@@ -123,22 +132,30 @@ class Stree(BaseEstimator, ClassifierMixin):
X = check_array(X)
# setup prediction & make it happen
indices = np.arange(X.shape[0])
return predict_class(X, indices, self._tree)
def _reorder_results(self, y: np.array, indices: np.array) -> np.array:
y_ordered = np.zeros(y.shape, dtype=int if y.ndim == 1 else float)
indices = indices.astype(int)
for i, index in enumerate(indices):
y_ordered[index] = y[i]
return y_ordered
def predict(self, X: np.array) -> np.array:
return self._reorder_results(*self._predict_values(X))
return self._reorder_results(*predict_class(X, indices, self._tree))
def predict_proba(self, X: np.array) -> np.array:
self.__proba = True
result, indices = self._predict_values(X)
self.__proba = False
def predict_class(xp: np.array, indices: np.array, dist: np.array, node: Snode) -> np.array:
if xp is None:
return [], []
if node.is_leaf():
# set a class for every sample in dataset
prediction = np.full((xp.shape[0], 1), node._class)
prediction_proba = np.full((xp.shape[0], 1), node._belief)
#prediction_proba = dist
#print("******", prediction.shape, prediction_proba.shape)
return np.append(prediction, prediction_proba, axis=1), indices
u, i_u, d, i_d, r_u, r_d = self._split_data(node, xp, indices)
k, l = predict_class(d, i_d, r_u, node.get_down())
m, n = predict_class(u, i_u, r_d, node.get_up())
return np.append(k, m), np.append(l, n)
# sklearn check
check_is_fitted(self)
# Input validation
X = check_array(X)
# setup prediction & make it happen
indices = np.arange(X.shape[0])
result, indices = predict_class(X, indices, [], self._tree)
result = result.reshape(X.shape[0], 2)
# Sigmoidize distance like in sklearn based on Platt(1999)
#result[:, 1] = 1 / (1 + np.exp(-result[:, 1]))