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stringlengths 2
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⌀ | name
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| docstring
stringlengths 0
281k
⌀ | code
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8.19k
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⌀ | embed_func_code
sequencelengths 768
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17,926 | imodels.rule_set.slipper_util | _prune_rule | Remove conditions from greedily built rule until
objective does not improve
| def _prune_rule(self, X, y, rule):
""" Remove conditions from greedily built rule until
objective does not improve
"""
stop_condition = False
curr_rule = rule.copy()
while not stop_condition:
candidate_rules = []
if len(curr_rule) == 1:
return curr_rule
candidate_rules = [
self._pop_condition(curr_rule, condition)
for condition in curr_rule
]
prune_objs = [self._prune_rule_obj(X, y, rule) for x in candidate_rules]
best_prune = candidate_rules[
prune_objs.index(min(prune_objs))
]
if self._prune_rule_obj(X, y, rule) > self._prune_rule_obj(X, y, rule):
curr_rule = best_prune.copy()
else:
stop_condition = True
return curr_rule
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17,927 | imodels.rule_set.slipper_util | _prune_rule_obj |
objective function for prune rule routine
eq 7 from Cohen & Singer (1999)
| def _prune_rule_obj(self, X, y, rule):
"""
objective function for prune rule routine
eq 7 from Cohen & Singer (1999)
"""
V_plus, V_minus = self._get_design_matrices(X, y, rule)
C_R = self._sample_weight(V_plus, V_minus)
return (1 - V_plus - V_minus) + V_plus * np.exp(-C_R) \
+ V_minus * np.exp(C_R)
| (self, X, y, rule) | [
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17,930 | imodels.rule_set.slipper_util | _rule_predict | return all indices for which the passed rule holds on X | def _rule_predict(self, X, rule):
""" return all indices for which the passed rule holds on X """
preds = np.zeros(X.shape[0])
positive_cases = set(range(X.shape[0]))
for condition in rule:
outputs = set(list(self._condition_classify(X, condition)))
positive_cases = positive_cases.intersection(outputs)
preds[list(positive_cases)] = 1
return preds
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17,931 | imodels.rule_set.slipper_util | _sample_weight | Calculate learner sample weight
in paper this is C_R, which is confidence of learner
| def _sample_weight(self, plus, minus):
""" Calculate learner sample weight
in paper this is C_R, which is confidence of learner
"""
return 0.5 * np.log((plus + (1 / (2 * len(self.D)))) /
(minus + 1 / (2 * len(self.D))))
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17,932 | imodels.rule_set.slipper_util | _set_rule_or_default |
Compare output of eq 5 between learned rule and default rule
return rule that minimizes eq 5
| def _set_rule_or_default(self, X, y, learned_rule):
"""
Compare output of eq 5 between learned rule and default rule
return rule that minimizes eq 5
"""
rules = [self._make_default_rule(X, y), learned_rule]
scores = [self._eq_5(X, y, rule) for rule in rules]
self.rule = rules[scores.index(min(scores))]
| (self, X, y, learned_rule) | [
0.03968759626150131,
-0.003676379332318902,
0.01627122238278389,
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17,935 | imodels.rule_set.slipper_util | fit |
Main loop for training
| def fit(self, X, y, sample_weight=None, feature_names=None):
"""
Main loop for training
"""
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
if sample_weight is not None:
self.D = sample_weight
X_grow, X_prune, y_grow, y_prune = \
train_test_split(X, y, test_size=0.33)
self._make_feature_dict(X.shape[1], feature_names)
rule = self._grow_rule(X_grow, y_grow)
rule = self._prune_rule(X_prune, y_prune, rule)
self._set_rule_or_default(X, y, rule)
return self
| (self, X, y, sample_weight=None, feature_names=None) | [
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17,938 | imodels.rule_set.slipper_util | predict |
external predict function that returns predictions
using estimators rule
| def predict(self, X):
"""
external predict function that returns predictions
using estimators rule
"""
return self._rule_predict(X, self.rule)
| (self, X) | [
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17,939 | imodels.rule_set.slipper_util | predict_proba | null | def predict_proba(self, X):
proba = self.predict(X)
proba = proba.reshape(-1, 1)
proba = np.hstack([
np.zeros(proba.shape), proba
])
return proba
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17,944 | imodels.rule_set.slipper | SlipperClassifier | null | class SlipperClassifier(BoostedRulesClassifier):
def __init__(self, n_estimators=10, **kwargs):
'''
An estimator that supports building rules as described in
A Simple, Fast, and Effective Rule Learner (1999).
Parameters
----------
n_estimators
'''
super().__init__(estimator=SlipperBaseEstimator(), n_estimators=n_estimators, **kwargs)
# super().__init__(n_estimators, SlipperBaseEstimator)
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17,947 | imodels.rule_set.slipper | __init__ |
An estimator that supports building rules as described in
A Simple, Fast, and Effective Rule Learner (1999).
Parameters
----------
n_estimators
| def __init__(self, n_estimators=10, **kwargs):
'''
An estimator that supports building rules as described in
A Simple, Fast, and Effective Rule Learner (1999).
Parameters
----------
n_estimators
'''
super().__init__(estimator=SlipperBaseEstimator(), n_estimators=n_estimators, **kwargs)
# super().__init__(n_estimators, SlipperBaseEstimator)
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17,985 | imodels.tree.tao | TaoTreeClassifier | null | class TaoTreeClassifier(TaoTree, ClassifierMixin):
pass
| (model_type: str = 'CART', n_iters: int = 20, model_args: dict = {'max_leaf_nodes': 15}, randomize_tree=False, update_scoring='accuracy', min_node_samples_tao=3, min_leaf_samples_tao=2, node_model='stump', node_model_args: dict = {}, reg_param: float = 0.001, weight_errors: bool = False, verbose: int = 0) | [
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17,987 | imodels.tree.tao | __init__ | TAO: Alternating optimization of decision trees, with application to learning sparse oblique trees (Neurips 2018)
https://proceedings.neurips.cc/paper/2018/hash/185c29dc24325934ee377cfda20e414c-Abstract.html
Note: this implementation learns single-feature splits rather than oblique trees.
Currently supports
- given a CART tree, posthoc improve it with TAO
- also works with HSTreeCV
Todo
- update bottom to top otherwise input points don't get updated
- update leaf nodes
- support regression
- support FIGS
- support error-weighting
- support oblique trees
- support generic models at decision node
- support pruning (e.g. if weights -> 0, then remove a node)
- support classifiers in leaves
Parameters
----------
model_type: str
'CART' or 'FIGS'
n_iters
Number of iterations to run TAO
model_args
Arguments to pass to the model
randomize_tree
Whether to randomize the tree before each iteration
min_node_samples_tao: int
Minimum number of samples in a node to apply tao
min_leaf_samples_tao: int
node_model: str
'stump' or 'linear'
reg_param
Regularization parameter for node-wise linear model (if node_model is 'linear')
verbose: int
Verbosity level
| def __init__(self, model_type: str = 'CART',
n_iters: int = 20,
model_args: dict = {'max_leaf_nodes': 15},
randomize_tree=False,
update_scoring='accuracy',
min_node_samples_tao=3,
min_leaf_samples_tao=2,
node_model='stump',
node_model_args: dict = {},
reg_param: float = 1e-3,
weight_errors: bool = False,
verbose: int = 0,
):
"""TAO: Alternating optimization of decision trees, with application to learning sparse oblique trees (Neurips 2018)
https://proceedings.neurips.cc/paper/2018/hash/185c29dc24325934ee377cfda20e414c-Abstract.html
Note: this implementation learns single-feature splits rather than oblique trees.
Currently supports
- given a CART tree, posthoc improve it with TAO
- also works with HSTreeCV
Todo
- update bottom to top otherwise input points don't get updated
- update leaf nodes
- support regression
- support FIGS
- support error-weighting
- support oblique trees
- support generic models at decision node
- support pruning (e.g. if weights -> 0, then remove a node)
- support classifiers in leaves
Parameters
----------
model_type: str
'CART' or 'FIGS'
n_iters
Number of iterations to run TAO
model_args
Arguments to pass to the model
randomize_tree
Whether to randomize the tree before each iteration
min_node_samples_tao: int
Minimum number of samples in a node to apply tao
min_leaf_samples_tao: int
node_model: str
'stump' or 'linear'
reg_param
Regularization parameter for node-wise linear model (if node_model is 'linear')
verbose: int
Verbosity level
"""
super().__init__()
self.model_type = model_type
self.n_iters = n_iters
self.model_args = model_args
self.randomize_tree = randomize_tree
self.update_scoring = update_scoring
self.min_node_samples_tao = min_node_samples_tao
self.min_leaf_samples_tao = min_leaf_samples_tao
self.node_model = node_model
self.node_model_args = node_model_args
self.reg_param = reg_param
self.weight_errors = weight_errors
self.verbose = verbose
self._init_prediction_task() # decides between regressor and classifier
| (self, model_type: str = 'CART', n_iters: int = 20, model_args: dict = {'max_leaf_nodes': 15}, randomize_tree=False, update_scoring='accuracy', min_node_samples_tao=3, min_leaf_samples_tao=2, node_model='stump', node_model_args: dict = {}, reg_param: float = 0.001, weight_errors: bool = False, verbose: int = 0) | [
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] |
17,996 | imodels.tree.tao | _init_prediction_task |
TaoRegressor and TaoClassifier override this method
to alter the prediction task. When using this class directly,
it is equivalent to SuperCARTRegressor
| def _init_prediction_task(self):
"""
TaoRegressor and TaoClassifier override this method
to alter the prediction task. When using this class directly,
it is equivalent to SuperCARTRegressor
"""
self.prediction_task = 'classification'
| (self) | [
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18,000 | imodels.tree.tao | _tao_iter_cart | Updates tree by applying the tao algorithm to the tree
Params
------
X: array-like of shape (n_samples, n_features)
The input samples.
y: array-like of shape (n_samples,)
The target values.
model: DecisionTreeClassifier.tree_ or DecisionTreeRegressor.tree_
The model to be post-hoc improved
| def _tao_iter_cart(self, X, y, tree, X_score=None, y_score=None, sample_weight=None):
"""Updates tree by applying the tao algorithm to the tree
Params
------
X: array-like of shape (n_samples, n_features)
The input samples.
y: array-like of shape (n_samples,)
The target values.
model: DecisionTreeClassifier.tree_ or DecisionTreeRegressor.tree_
The model to be post-hoc improved
"""
# Tree properties
children_left = tree.children_left
children_right = tree.children_right
feature = tree.feature
threshold = tree.threshold
value = tree.value
# For each node, store the path to that node #######################################################
indexes_with_prefix_paths = [] # data structure with (index, path_to_node_index)
# e.g. if if node 3 is the left child of node 1 which is the right child of node 0
# then we get (3, [(0, R), (1, L)])
# start with the root node id (0) and its depth (0)
queue = deque()
queue.append((0, []))
while len(queue) > 0:
node_id, path_to_node_index = queue.popleft()
indexes_with_prefix_paths.append((node_id, path_to_node_index))
# If a split node, append left and right children and depth to queue
if children_left[node_id] != children_right[node_id]:
queue.append((children_left[node_id], path_to_node_index + [(node_id, 'L')]))
queue.append((children_right[node_id], path_to_node_index + [(node_id, 'R')]))
# print(indexes_with_prefix_paths)
num_updates = 0
# Reversing BFS queue presents nodes bottom -> top one level at a time
for (node_id, path_to_node_index) in reversed(indexes_with_prefix_paths):
# For each each node, try a TAO update
# print('node_id', node_id, path_to_node_index)
# Compute the points being input to the node ######################################
def filter_points_by_path(X, y, path_to_node_index):
"""Returns the points in X that are in the path to the node"""
for node_id, direction in path_to_node_index:
idxs = X[:, feature[node_id]] <= threshold[node_id]
if direction == 'R':
idxs = ~idxs
# print('idxs', idxs.size, idxs.sum())
X = X[idxs]
y = y[idxs]
return X, y
X_node, y_node = filter_points_by_path(X, y, path_to_node_index)
if sample_weight is not None:
sample_weight_node = filter_points_by_path(X, sample_weight, path_to_node_index)[1]
else:
sample_weight_node = np.ones(y_node.size)
# Skip over leaf nodes and nodes with too few samples ######################################
if children_left[node_id] == children_right[node_id]: # is leaf node
if isinstance(self, RegressorMixin) and X_node.shape[0] >= self.min_leaf_samples_tao:
# old_score = self.model.score(X, y)
value[node_id] = np.mean(y_node)
"""
new_score = self.model.score(X, y)
if new_score > old_score:
print(f'\tLeaf improved score from {old_score:0.3f} to {new_score:0.3f}')
if new_score < old_score:
print(f'\tLeaf reduced score from {old_score:0.3f} to {new_score:0.3f}')
# raise ValueError('Leaf update reduced score')
"""
# print('\tshapes', X_node.shape, y_node.shape)
# print('\tvals:', value[node_id][0][0], np.mean(y_node))
# assert value[node_id][0][0] == np.mean(y_node), 'unless tree changed, vals should be leaf means'
continue
elif X_node.shape[0] < self.min_node_samples_tao:
continue
# Compute the outputs for these points if they go left or right ######################################
def predict_from_node(X, node_id):
"""Returns predictions for X starting at node node_id"""
def predict_from_node(x, node_id):
"""Returns predictions for x starting at node node_id"""
if children_left[node_id] == children_right[node_id]:
if isinstance(self, RegressorMixin):
return value[node_id]
if isinstance(self, ClassifierMixin):
return np.argmax(value[node_id]) # note value stores counts for each class
if x[feature[node_id]] <= threshold[node_id]:
return predict_from_node(x, children_left[node_id])
else:
return predict_from_node(x, children_right[node_id])
preds = np.zeros(X.shape[0])
for i in range(X.shape[0]):
preds[i] = predict_from_node(X[i], node_id)
return preds
y_node_left = predict_from_node(X_node, children_left[node_id])
y_node_right = predict_from_node(X_node, children_right[node_id])
if node_id == 0: # root node
assert np.all(np.logical_or(self.model.predict(X_node) == y_node_left,
self.model.predict(
X_node) == y_node_right)), \
'actual predictions should match either predict_from_node left or right'
# Decide on prediction target (want to go left (0) / right (1) when advantageous)
# TAO paper binarizes these (e.g. predict 0 or 1 depending on which of these is better)
y_node_absolute_errors = np.abs(np.vstack((y_node - y_node_left,
y_node - y_node_right))).T
# screen out indexes where going left/right has no effect
idxs_relevant = y_node_absolute_errors[:, 0] != y_node_absolute_errors[:, 1]
if idxs_relevant.sum() <= 1: # nothing to change
if self.verbose:
print('no errors to change')
continue
# assert np.all((self.model.predict(X) != y)[idxs_relevant]), 'relevant indexes should be errors'
y_node_target = np.argmin(y_node_absolute_errors, axis=1)
y_node_target = y_node_target[idxs_relevant]
# here, we optionally weight these errors by the size of the error
# if we want this to work for classification, must switch to predict_proba
# if self.prediction_task == 'regression':
# weight by the difference in error ###############################################################
if self.weight_errors:
sample_weight_node *= np.abs(y_node_absolute_errors[:, 1] - y_node_absolute_errors[:, 0])
sample_weight_node_target = sample_weight_node[idxs_relevant]
X_node = X_node[idxs_relevant]
# Fit a 1-variable binary classification model on these outputs ######################################
# Note: this could be customized (e.g. for sparse oblique trees)
best_score = -np.inf
best_feat_num = None
for feat_num in range(X.shape[1]):
if isinstance(self, ClassifierMixin):
if self.node_model == 'linear':
m = LogisticRegression(**self.node_model_args)
elif self.node_model == 'stump':
m = DecisionTreeClassifier(max_depth=1, **self.node_model_args)
if isinstance(self, RegressorMixin):
if self.node_model == 'linear':
m = LinearRegression(**self.node_model_args)
elif self.node_model == 'stump':
m = DecisionTreeRegressor(max_depth=1, **self.node_model_args)
X_node_single_feat = X_node[:, feat_num: feat_num + 1]
m.fit(X_node_single_feat, y_node_target, sample_weight=sample_weight_node_target)
score = m.score(X_node_single_feat, y_node_target, sample_weight=sample_weight_node_target)
if score > best_score:
best_score = score
best_feat_num = feat_num
best_model = deepcopy(m)
if self.node_model == 'linear':
best_threshold = -best_model.intercep | (self, X, y, tree, X_score=None, y_score=None, sample_weight=None) | [
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] |
18,003 | imodels.tree.tao | fit |
Params
------
_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.
| def fit(self, X, y=None, feature_names=None, sample_weight=None):
"""
Params
------
_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.
"""
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
if isinstance(self, RegressorMixin):
raise Warning('TAO Regression is not yet tested')
X, y = check_X_y(X, y)
y = y.astype(float)
if feature_names is not None:
self.feature_names_ = feature_names
if self.model_type == 'CART':
if isinstance(self, ClassifierMixin):
self.model = DecisionTreeClassifier(**self.model_args)
elif isinstance(self, RegressorMixin):
self.model = DecisionTreeRegressor(**self.model_args)
self.model.fit(X, y, sample_weight=sample_weight)
if self.verbose:
print(export_text(self.model))
# plot_tree(self.model)
# plt.savefig('/Users/chandan/Desktop/tree.png', dpi=300)
# plt.show()
if self.randomize_tree:
np.random.shuffle(self.model.tree_.feature) # shuffle CART features
# np.random.shuffle(self.model.tree_.threshold)
for i in range(self.model.tree_.node_count): # split on feature medians
self.model.tree_.threshold[i] = np.median(
X[:, self.model.tree_.feature[i]])
if self.verbose:
print('starting score', self.model.score(X, y))
for i in range(self.n_iters):
num_updates = self._tao_iter_cart(X, y, self.model.tree_, sample_weight=sample_weight)
if num_updates == 0:
break
return self
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18,006 | imodels.tree.tao | predict | null | def predict(self, X):
return self.model.predict(X)
| (self, X) | [
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18,007 | imodels.tree.tao | predict_proba | null | def predict_proba(self, X):
return self.model.predict_proba(X)
| (self, X) | [
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18,008 | imodels.tree.tao | score | null | def score(self, X, y):
return self.model.score(X, y)
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18,011 | imodels.tree.tao | TaoTreeRegressor | null | class TaoTreeRegressor(TaoTree, RegressorMixin):
pass
| (model_type: str = 'CART', n_iters: int = 20, model_args: dict = {'max_leaf_nodes': 15}, randomize_tree=False, update_scoring='accuracy', min_node_samples_tao=3, min_leaf_samples_tao=2, node_model='stump', node_model_args: dict = {}, reg_param: float = 0.001, weight_errors: bool = False, verbose: int = 0) | [
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] |
18,037 | imodels.algebraic.tree_gam | TreeGAMClassifier | null | class TreeGAMClassifier(TreeGAM, ClassifierMixin):
...
| (n_boosting_rounds=100, max_leaf_nodes=3, reg_param=0.0, learning_rate: float = 0.01, n_boosting_rounds_marginal=0, max_leaf_nodes_marginal=2, reg_param_marginal=0.0, fit_linear_marginal=None, select_linear_marginal=False, decay_rate_towards_marginal=1.0, fit_posthoc_tree_coefs=None, boosting_strategy='cyclic', validation_frac=0.15, random_state=None) | [
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18,039 | imodels.algebraic.tree_gam | __init__ |
Params
------
n_boosting_rounds : int
Number of boosting rounds for the cyclic boosting.
max_leaf_nodes : int
Maximum number of leaf nodes for the trees in the cyclic boosting.
reg_param : float
Regularization parameter for the cyclic boosting.
learning_rate: float
Learning rate for the cyclic boosting.
n_boosting_rounds_marginal : int
Number of boosting rounds for the marginal boosting.
max_leaf_nodes_marginal : int
Maximum number of leaf nodes for the trees in the marginal boosting.
reg_param_marginal : float
Regularization parameter for the marginal boosting.
fit_linear_marginal : str [None, "None", "ridge", "NNLS"]
Whether to fit a linear model to the marginal effects.
NNLS for non-negative least squares
ridge for ridge regression
None for no linear model
select_linear_marginal: bool
Whether to restrict features to those with non-negative coefficients in the linear model.
Requires that fit_linear_marginal is NNLS.
decay_rate_towards_marginal: float, [0, 1]
Decay rate for regularizing each shape function towards the marginal shape function after each step
1 means no decay, 0 means only use marginal effects
shape = (1 - decay_rate_towards_marginal) * shape + decay_rate_towards_marginal * marginal_shape
The way this is implemented is by keeping track of how many times to multiply decay_rate_towards_marginal for each cyclic estimator
fit_posthoc_tree_coefs: str [None, "ridge"]
Whether to fit a linear model to the tree coefficients after fitting the cyclic boosting.
boosting_strategy : str ["cyclic", "greedy"]
Whether to use cyclic boosting (cycle over features) or greedy boosting (select best feature at each step)
validation_frac: float
Fraction of data to use for early stopping.
random_state : int
Random seed.
| def __init__(
self,
n_boosting_rounds=100,
max_leaf_nodes=3,
reg_param=0.0,
learning_rate: float = 0.01,
n_boosting_rounds_marginal=0,
max_leaf_nodes_marginal=2,
reg_param_marginal=0.0,
fit_linear_marginal=None,
select_linear_marginal=False,
decay_rate_towards_marginal=1.0,
fit_posthoc_tree_coefs=None,
boosting_strategy="cyclic",
validation_frac=0.15,
random_state=None,
):
"""
Params
------
n_boosting_rounds : int
Number of boosting rounds for the cyclic boosting.
max_leaf_nodes : int
Maximum number of leaf nodes for the trees in the cyclic boosting.
reg_param : float
Regularization parameter for the cyclic boosting.
learning_rate: float
Learning rate for the cyclic boosting.
n_boosting_rounds_marginal : int
Number of boosting rounds for the marginal boosting.
max_leaf_nodes_marginal : int
Maximum number of leaf nodes for the trees in the marginal boosting.
reg_param_marginal : float
Regularization parameter for the marginal boosting.
fit_linear_marginal : str [None, "None", "ridge", "NNLS"]
Whether to fit a linear model to the marginal effects.
NNLS for non-negative least squares
ridge for ridge regression
None for no linear model
select_linear_marginal: bool
Whether to restrict features to those with non-negative coefficients in the linear model.
Requires that fit_linear_marginal is NNLS.
decay_rate_towards_marginal: float, [0, 1]
Decay rate for regularizing each shape function towards the marginal shape function after each step
1 means no decay, 0 means only use marginal effects
shape = (1 - decay_rate_towards_marginal) * shape + decay_rate_towards_marginal * marginal_shape
The way this is implemented is by keeping track of how many times to multiply decay_rate_towards_marginal for each cyclic estimator
fit_posthoc_tree_coefs: str [None, "ridge"]
Whether to fit a linear model to the tree coefficients after fitting the cyclic boosting.
boosting_strategy : str ["cyclic", "greedy"]
Whether to use cyclic boosting (cycle over features) or greedy boosting (select best feature at each step)
validation_frac: float
Fraction of data to use for early stopping.
random_state : int
Random seed.
"""
self.n_boosting_rounds = n_boosting_rounds
self.max_leaf_nodes = max_leaf_nodes
self.reg_param = reg_param
self.learning_rate = learning_rate
self.max_leaf_nodes_marginal = max_leaf_nodes_marginal
self.reg_param_marginal = reg_param_marginal
self.n_boosting_rounds_marginal = n_boosting_rounds_marginal
self.fit_linear_marginal = fit_linear_marginal
self.select_linear_marginal = select_linear_marginal
self.decay_rate_towards_marginal = decay_rate_towards_marginal
self.fit_posthoc_tree_coefs = fit_posthoc_tree_coefs
self.boosting_strategy = boosting_strategy
self.validation_frac = validation_frac
self.random_state = random_state
| (self, n_boosting_rounds=100, max_leaf_nodes=3, reg_param=0.0, learning_rate: float = 0.01, n_boosting_rounds_marginal=0, max_leaf_nodes_marginal=2, reg_param_marginal=0.0, fit_linear_marginal=None, select_linear_marginal=False, decay_rate_towards_marginal=1.0, fit_posthoc_tree_coefs=None, boosting_strategy='cyclic', validation_frac=0.15, random_state=None) | [
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18,043 | imodels.algebraic.tree_gam | _calc_mse | null | def _calc_mse(self, X, y, sample_weight=None):
return np.average(
np.square(y - self.predict_proba(X)[:, 1]),
weights=sample_weight,
)
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18,046 | imodels.algebraic.tree_gam | _cyclic_boost | Apply cyclic boosting, storing trees in self.estimators_ | def _cyclic_boost(
self, X_train, y_train, sample_weight_train, X_val, y_val, sample_weight_val
):
"""Apply cyclic boosting, storing trees in self.estimators_"""
residuals_train = y_train - self.predict_proba(X_train)[:, 1]
mse_val = self._calc_mse(X_val, y_val, sample_weight_val)
self.decay_coef_towards_marginal_ = []
for _ in range(self.n_boosting_rounds):
boosting_round_ests = []
boosting_round_mses = []
feature_nums = np.arange(X_train.shape[1])
if self.select_linear_marginal:
assert (
self.fit_linear_marginal == "NNLS"
and self.n_boosting_rounds_marginal > 0
), "select_linear_marginal requires fit_linear_marginal to be NNLS and for n_boosting_rounds_marginal > 0"
feature_nums = np.where(self.marginal_coef_ > 0)[0]
for feature_num in feature_nums:
X_ = np.zeros_like(X_train)
X_[:, feature_num] = X_train[:, feature_num]
est = DecisionTreeRegressor(
max_leaf_nodes=self.max_leaf_nodes,
random_state=self.random_state,
)
est.fit(X_, residuals_train, sample_weight=sample_weight_train)
succesfully_split_on_feature = np.all(
(est.tree_.feature[0] == feature_num) | (
est.tree_.feature[0] == -2)
)
if not succesfully_split_on_feature:
continue
if self.reg_param > 0:
est = imodels.HSTreeRegressor(
est, reg_param=self.reg_param)
self.estimators_.append(est)
residuals_train_new = (
residuals_train - self.learning_rate * est.predict(X_train)
)
if self.boosting_strategy == "cyclic":
residuals_train = residuals_train_new
elif self.boosting_strategy == "greedy":
mse_train_new = self._calc_mse(
X_train, y_train, sample_weight_train
)
# don't add each estimator for greedy
boosting_round_ests.append(
deepcopy(self.estimators_.pop()))
boosting_round_mses.append(mse_train_new)
if self.boosting_strategy == "greedy":
best_est = boosting_round_ests[np.argmin(boosting_round_mses)]
self.estimators_.append(best_est)
residuals_train = (
residuals_train - self.learning_rate *
best_est.predict(X_train)
)
# decay marginal effects
if self.decay_rate_towards_marginal < 1.0:
new_decay_coefs = [self.decay_rate_towards_marginal] * (
len(self.estimators_) -
len(self.decay_coef_towards_marginal_)
)
# print(self.decay_coef_towards_marginal_)
# print('new_decay_coefs', new_decay_coefs)
self.decay_coef_towards_marginal_ = [
x * self.decay_rate_towards_marginal
for x in self.decay_coef_towards_marginal_
] + new_decay_coefs
# print(self.decay_coef_towards_marginal_)
# early stopping if validation error does not decrease
mse_val_new = self._calc_mse(X_val, y_val, sample_weight_val)
if mse_val_new >= mse_val:
# print("early stop!")
return
else:
mse_val = mse_val_new
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18,047 | imodels.algebraic.tree_gam | _fit_posthoc_tree_coefs | null | def _fit_posthoc_tree_coefs(self, X, y, sample_weight=None):
# extract predictions from each tree
X_pred_tree = np.array([est.predict(X) for est in self.estimators_]).T
print('shapes', X.shape, X_pred_tree.shape,
y.shape, len(self.estimators_))
coef_prior = np.ones(len(self.estimators_)) * self.learning_rate
y = y - self.bias_ - X_pred_tree @ coef_prior
if self.fit_posthoc_tree_coefs.lower() == "ridge":
m = RidgeCV(fit_intercept=False)
elif self.fit_posthoc_tree_coefs.lower() == "nnls":
m = LinearRegression(fit_intercept=False, positive=True)
elif self.fit_posthoc_tree_coefs.lower() == "elasticnet":
m = ElasticNetCV(fit_intercept=False, positive=True)
m.fit(X_pred_tree, y, sample_weight=sample_weight)
self.cyclic_coef_ = m.coef_ + coef_prior
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18,051 | imodels.algebraic.tree_gam | _marginal_fit | Fit a gbdt estimator for each feature independently.
Store in self.estimators_marginal | def _marginal_fit(
self,
X_train,
y_train,
sample_weight_train,
):
"""Fit a gbdt estimator for each feature independently.
Store in self.estimators_marginal"""
residuals_train = y_train - self.predict_proba(X_train)[:, 1]
p = X_train.shape[1]
for feature_num in range(p):
X_ = np.zeros_like(X_train)
X_[:, feature_num] = X_train[:, feature_num]
est = GradientBoostingRegressor(
max_leaf_nodes=self.max_leaf_nodes_marginal,
random_state=self.random_state,
n_estimators=self.n_boosting_rounds_marginal,
)
est.fit(X_, residuals_train, sample_weight=sample_weight_train)
if self.reg_param_marginal > 0:
est = imodels.HSTreeRegressor(
est, reg_param=self.reg_param_marginal)
self.estimators_marginal.append(est)
if (
self.fit_linear_marginal is not None
and not self.fit_linear_marginal == "None"
):
if self.fit_linear_marginal.lower() == "ridge":
linear_marginal = RidgeCV(fit_intercept=False)
elif self.fit_linear_marginal == "NNLS":
linear_marginal = LinearRegression(
fit_intercept=False, positive=True)
linear_marginal.fit(
np.array([est.predict(X_train)
for est in self.estimators_marginal]).T,
residuals_train,
sample_weight_train,
)
self.marginal_coef_ = linear_marginal.coef_
self.lin = linear_marginal
else:
self.marginal_coef_ = np.ones(p) / p
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18,057 | imodels.algebraic.tree_gam | fit | null | def fit(self, X, y, sample_weight=None):
X, y = check_X_y(X, y, accept_sparse=False, multi_output=False)
if isinstance(self, ClassifierMixin):
check_classification_targets(y)
self.classes_, y = np.unique(y, return_inverse=True)
sample_weight = _check_sample_weight(sample_weight, X, dtype=None)
# split into train and validation for early stopping
(
X_train,
X_val,
y_train,
y_val,
sample_weight_train,
sample_weight_val,
) = train_test_split(
X,
y,
sample_weight,
test_size=self.validation_frac,
random_state=self.random_state,
stratify=y if isinstance(self, ClassifierMixin) else None,
)
self.estimators_marginal = []
self.estimators_ = []
self.bias_ = np.mean(y)
if self.n_boosting_rounds_marginal > 0:
self._marginal_fit(
X_train,
y_train,
sample_weight_train,
)
if self.n_boosting_rounds > 0:
self._cyclic_boost(
X_train,
y_train,
sample_weight_train,
X_val,
y_val,
sample_weight_val,
)
if self.fit_posthoc_tree_coefs is not None:
self._fit_posthoc_tree_coefs(X_train, y_train, sample_weight_train)
self.mse_val_ = self._calc_mse(X_val, y_val, sample_weight_val)
return self
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18,060 | imodels.algebraic.tree_gam | predict | null | def predict(self, X, marginal_only=False):
if isinstance(self, RegressorMixin):
return self.predict_proba(X, marginal_only=marginal_only)[:, 1]
elif isinstance(self, ClassifierMixin):
return np.argmax(self.predict_proba(X, marginal_only=marginal_only), axis=1)
| (self, X, marginal_only=False) | [
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18,061 | imodels.algebraic.tree_gam | predict_proba |
Params
------
marginal_only: bool
If True, only use the marginal effects.
| def predict_proba(self, X, marginal_only=False):
"""
Params
------
marginal_only: bool
If True, only use the marginal effects.
"""
X = check_array(X, accept_sparse=False, dtype=None)
check_is_fitted(self)
probs1 = np.ones(X.shape[0]) * self.bias_
# marginal prediction
for i, est in enumerate(self.estimators_marginal):
probs1 += est.predict(X) * self.marginal_coef_[i]
# cyclic coefs prediction
if not marginal_only:
if not hasattr(self, "cyclic_coef_"):
cyclic_coef_ = np.ones(
len(self.estimators_)) * self.learning_rate
else:
cyclic_coef_ = self.cyclic_coef_
# print('coef', cyclic_coef_)
if self.decay_rate_towards_marginal < 1.0:
for i, est in enumerate(self.estimators_):
if i < len(self.decay_coef_towards_marginal_):
probs1 += (
cyclic_coef_[i]
* self.decay_coef_towards_marginal_[i]
* est.predict(X)
)
else:
probs1 += cyclic_coef_[i] * est.predict(X)
else:
for i, est in enumerate(self.estimators_):
probs1 += cyclic_coef_[i] * est.predict(X)
probs1 = np.clip(probs1, a_min=0, a_max=1)
return np.array([1 - probs1, probs1]).T
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18,065 | sklearn.utils._metadata_requests | set_predict_proba_request | Request metadata passed to the ``predict_proba`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``predict_proba`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``predict_proba``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
marginal_only : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``marginal_only`` parameter in ``predict_proba``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.algebraic.tree_gam.TreeGAMClassifier, *, marginal_only: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.algebraic.tree_gam.TreeGAMClassifier | [
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18,066 | sklearn.utils._metadata_requests | set_predict_request | Request metadata passed to the ``predict`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter are:
- ``True``: metadata is requested, and passed to ``predict`` if provided. The request is ignored if metadata is not provided.
- ``False``: metadata is not requested and the meta-estimator will not pass it to ``predict``.
- ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
- ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the
existing request. This allows you to change the request for some
parameters and not others.
.. versionadded:: 1.3
.. note::
This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.
Parameters
----------
marginal_only : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``marginal_only`` parameter in ``predict``.
Returns
-------
self : object
The updated object.
| def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance
# Now we set the relevant attributes of the function so that it seems
# like a normal method to the end user, with known expected arguments.
func.__name__ = f"set_{self.name}_request"
params = [
inspect.Parameter(
name="self",
kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=owner,
)
]
params.extend(
[
inspect.Parameter(
k,
inspect.Parameter.KEYWORD_ONLY,
default=UNCHANGED,
annotation=Optional[Union[bool, None, str]],
)
for k in self.keys
]
)
func.__signature__ = inspect.Signature(
params,
return_annotation=owner,
)
doc = REQUESTER_DOC.format(method=self.name)
for metadata in self.keys:
doc += REQUESTER_DOC_PARAM.format(metadata=metadata, method=self.name)
doc += REQUESTER_DOC_RETURN
func.__doc__ = doc
return func
| (self: imodels.algebraic.tree_gam.TreeGAMClassifier, *, marginal_only: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.algebraic.tree_gam.TreeGAMClassifier | [
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18,068 | imodels.algebraic.tree_gam | TreeGAMRegressor | null | class TreeGAMRegressor(TreeGAM, RegressorMixin):
...
| (n_boosting_rounds=100, max_leaf_nodes=3, reg_param=0.0, learning_rate: float = 0.01, n_boosting_rounds_marginal=0, max_leaf_nodes_marginal=2, reg_param_marginal=0.0, fit_linear_marginal=None, select_linear_marginal=False, decay_rate_towards_marginal=1.0, fit_posthoc_tree_coefs=None, boosting_strategy='cyclic', validation_frac=0.15, random_state=None) | [
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18,101 | sklearn.utils.validation | check_X_y | Input validation for standard estimators.
Checks X and y for consistent length, enforces X to be 2D and y 1D. By
default, X is checked to be non-empty and containing only finite values.
Standard input checks are also applied to y, such as checking that y
does not have np.nan or np.inf targets. For multi-label y, set
multi_output=True to allow 2D and sparse y. If the dtype of X is
object, attempt converting to float, raising on failure.
Parameters
----------
X : {ndarray, list, sparse matrix}
Input data.
y : {ndarray, list, sparse matrix}
Labels.
accept_sparse : str, bool or list of str, default=False
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. If the input is sparse but not in the allowed format,
it will be converted to the first listed format. True allows the input
to be any format. False means that a sparse matrix input will
raise an error.
accept_large_sparse : bool, default=True
If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
accept_sparse, accept_large_sparse will cause it to be accepted only
if its indices are stored with a 32-bit dtype.
.. versionadded:: 0.20
dtype : 'numeric', type, list of type or None, default='numeric'
Data type of result. If None, the dtype of the input is preserved.
If "numeric", dtype is preserved unless array.dtype is object.
If dtype is a list of types, conversion on the first type is only
performed if the dtype of the input is not in the list.
order : {'F', 'C'}, default=None
Whether an array will be forced to be fortran or c-style. If
`None`, then the input data's order is preserved when possible.
copy : bool, default=False
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : bool or 'allow-nan', default=True
Whether to raise an error on np.inf, np.nan, pd.NA in X. This parameter
does not influence whether y can have np.inf, np.nan, pd.NA values.
The possibilities are:
- True: Force all values of X to be finite.
- False: accepts np.inf, np.nan, pd.NA in X.
- 'allow-nan': accepts only np.nan or pd.NA values in X. Values cannot
be infinite.
.. versionadded:: 0.20
``force_all_finite`` accepts the string ``'allow-nan'``.
.. versionchanged:: 0.23
Accepts `pd.NA` and converts it into `np.nan`
ensure_2d : bool, default=True
Whether to raise a value error if X is not 2D.
allow_nd : bool, default=False
Whether to allow X.ndim > 2.
multi_output : bool, default=False
Whether to allow 2D y (array or sparse matrix). If false, y will be
validated as a vector. y cannot have np.nan or np.inf values if
multi_output=True.
ensure_min_samples : int, default=1
Make sure that X has a minimum number of samples in its first
axis (rows for a 2D array).
ensure_min_features : int, default=1
Make sure that the 2D array has some minimum number of features
(columns). The default value of 1 rejects empty datasets.
This check is only enforced when X has effectively 2 dimensions or
is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
this check.
y_numeric : bool, default=False
Whether to ensure that y has a numeric type. If dtype of y is object,
it is converted to float64. Should only be used for regression
algorithms.
estimator : str or estimator instance, default=None
If passed, include the name of the estimator in warning messages.
Returns
-------
X_converted : object
The converted and validated X.
y_converted : object
The converted and validated y.
Examples
--------
>>> from sklearn.utils.validation import check_X_y
>>> X = [[1, 2], [3, 4], [5, 6]]
>>> y = [1, 2, 3]
>>> X, y = check_X_y(X, y)
>>> X
array([[1, 2],
[3, 4],
[5, 6]])
>>> y
array([1, 2, 3])
| def check_X_y(
X,
y,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_all_finite=True,
ensure_2d=True,
allow_nd=False,
multi_output=False,
ensure_min_samples=1,
ensure_min_features=1,
y_numeric=False,
estimator=None,
):
"""Input validation for standard estimators.
Checks X and y for consistent length, enforces X to be 2D and y 1D. By
default, X is checked to be non-empty and containing only finite values.
Standard input checks are also applied to y, such as checking that y
does not have np.nan or np.inf targets. For multi-label y, set
multi_output=True to allow 2D and sparse y. If the dtype of X is
object, attempt converting to float, raising on failure.
Parameters
----------
X : {ndarray, list, sparse matrix}
Input data.
y : {ndarray, list, sparse matrix}
Labels.
accept_sparse : str, bool or list of str, default=False
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. If the input is sparse but not in the allowed format,
it will be converted to the first listed format. True allows the input
to be any format. False means that a sparse matrix input will
raise an error.
accept_large_sparse : bool, default=True
If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
accept_sparse, accept_large_sparse will cause it to be accepted only
if its indices are stored with a 32-bit dtype.
.. versionadded:: 0.20
dtype : 'numeric', type, list of type or None, default='numeric'
Data type of result. If None, the dtype of the input is preserved.
If "numeric", dtype is preserved unless array.dtype is object.
If dtype is a list of types, conversion on the first type is only
performed if the dtype of the input is not in the list.
order : {'F', 'C'}, default=None
Whether an array will be forced to be fortran or c-style. If
`None`, then the input data's order is preserved when possible.
copy : bool, default=False
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : bool or 'allow-nan', default=True
Whether to raise an error on np.inf, np.nan, pd.NA in X. This parameter
does not influence whether y can have np.inf, np.nan, pd.NA values.
The possibilities are:
- True: Force all values of X to be finite.
- False: accepts np.inf, np.nan, pd.NA in X.
- 'allow-nan': accepts only np.nan or pd.NA values in X. Values cannot
be infinite.
.. versionadded:: 0.20
``force_all_finite`` accepts the string ``'allow-nan'``.
.. versionchanged:: 0.23
Accepts `pd.NA` and converts it into `np.nan`
ensure_2d : bool, default=True
Whether to raise a value error if X is not 2D.
allow_nd : bool, default=False
Whether to allow X.ndim > 2.
multi_output : bool, default=False
Whether to allow 2D y (array or sparse matrix). If false, y will be
validated as a vector. y cannot have np.nan or np.inf values if
multi_output=True.
ensure_min_samples : int, default=1
Make sure that X has a minimum number of samples in its first
axis (rows for a 2D array).
ensure_min_features : int, default=1
Make sure that the 2D array has some minimum number of features
(columns). The default value of 1 rejects empty datasets.
This check is only enforced when X has effectively 2 dimensions or
is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
this check.
y_numeric : bool, default=False
Whether to ensure that y has a numeric type. If dtype of y is object,
it is converted to float64. Should only be used for regression
algorithms.
estimator : str or estimator instance, default=None
If passed, include the name of the estimator in warning messages.
Returns
-------
X_converted : object
The converted and validated X.
y_converted : object
The converted and validated y.
Examples
--------
>>> from sklearn.utils.validation import check_X_y
>>> X = [[1, 2], [3, 4], [5, 6]]
>>> y = [1, 2, 3]
>>> X, y = check_X_y(X, y)
>>> X
array([[1, 2],
[3, 4],
[5, 6]])
>>> y
array([1, 2, 3])
"""
if y is None:
if estimator is None:
estimator_name = "estimator"
else:
estimator_name = _check_estimator_name(estimator)
raise ValueError(
f"{estimator_name} requires y to be passed, but the target y is None"
)
X = check_array(
X,
accept_sparse=accept_sparse,
accept_large_sparse=accept_large_sparse,
dtype=dtype,
order=order,
copy=copy,
force_all_finite=force_all_finite,
ensure_2d=ensure_2d,
allow_nd=allow_nd,
ensure_min_samples=ensure_min_samples,
ensure_min_features=ensure_min_features,
estimator=estimator,
input_name="X",
)
y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
check_consistent_length(X, y)
return X, y
| (X, y, accept_sparse=False, *, accept_large_sparse=True, dtype='numeric', order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, multi_output=False, ensure_min_samples=1, ensure_min_features=1, y_numeric=False, estimator=None) | [
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18,102 | sklearn.utils.validation | check_array | Input validation on an array, list, sparse matrix or similar.
By default, the input is checked to be a non-empty 2D array containing
only finite values. If the dtype of the array is object, attempt
converting to float, raising on failure.
Parameters
----------
array : object
Input object to check / convert.
accept_sparse : str, bool or list/tuple of str, default=False
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. If the input is sparse but not in the allowed format,
it will be converted to the first listed format. True allows the input
to be any format. False means that a sparse matrix input will
raise an error.
accept_large_sparse : bool, default=True
If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
accept_sparse, accept_large_sparse=False will cause it to be accepted
only if its indices are stored with a 32-bit dtype.
.. versionadded:: 0.20
dtype : 'numeric', type, list of type or None, default='numeric'
Data type of result. If None, the dtype of the input is preserved.
If "numeric", dtype is preserved unless array.dtype is object.
If dtype is a list of types, conversion on the first type is only
performed if the dtype of the input is not in the list.
order : {'F', 'C'} or None, default=None
Whether an array will be forced to be fortran or c-style.
When order is None (default), then if copy=False, nothing is ensured
about the memory layout of the output array; otherwise (copy=True)
the memory layout of the returned array is kept as close as possible
to the original array.
copy : bool, default=False
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : bool or 'allow-nan', default=True
Whether to raise an error on np.inf, np.nan, pd.NA in array. The
possibilities are:
- True: Force all values of array to be finite.
- False: accepts np.inf, np.nan, pd.NA in array.
- 'allow-nan': accepts only np.nan and pd.NA values in array. Values
cannot be infinite.
.. versionadded:: 0.20
``force_all_finite`` accepts the string ``'allow-nan'``.
.. versionchanged:: 0.23
Accepts `pd.NA` and converts it into `np.nan`
ensure_2d : bool, default=True
Whether to raise a value error if array is not 2D.
allow_nd : bool, default=False
Whether to allow array.ndim > 2.
ensure_min_samples : int, default=1
Make sure that the array has a minimum number of samples in its first
axis (rows for a 2D array). Setting to 0 disables this check.
ensure_min_features : int, default=1
Make sure that the 2D array has some minimum number of features
(columns). The default value of 1 rejects empty datasets.
This check is only enforced when the input data has effectively 2
dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
disables this check.
estimator : str or estimator instance, default=None
If passed, include the name of the estimator in warning messages.
input_name : str, default=""
The data name used to construct the error message. In particular
if `input_name` is "X" and the data has NaN values and
allow_nan is False, the error message will link to the imputer
documentation.
.. versionadded:: 1.1.0
Returns
-------
array_converted : object
The converted and validated array.
Examples
--------
>>> from sklearn.utils.validation import check_array
>>> X = [[1, 2, 3], [4, 5, 6]]
>>> X_checked = check_array(X)
>>> X_checked
array([[1, 2, 3], [4, 5, 6]])
| def check_array(
array,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_all_finite=True,
ensure_2d=True,
allow_nd=False,
ensure_min_samples=1,
ensure_min_features=1,
estimator=None,
input_name="",
):
"""Input validation on an array, list, sparse matrix or similar.
By default, the input is checked to be a non-empty 2D array containing
only finite values. If the dtype of the array is object, attempt
converting to float, raising on failure.
Parameters
----------
array : object
Input object to check / convert.
accept_sparse : str, bool or list/tuple of str, default=False
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. If the input is sparse but not in the allowed format,
it will be converted to the first listed format. True allows the input
to be any format. False means that a sparse matrix input will
raise an error.
accept_large_sparse : bool, default=True
If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
accept_sparse, accept_large_sparse=False will cause it to be accepted
only if its indices are stored with a 32-bit dtype.
.. versionadded:: 0.20
dtype : 'numeric', type, list of type or None, default='numeric'
Data type of result. If None, the dtype of the input is preserved.
If "numeric", dtype is preserved unless array.dtype is object.
If dtype is a list of types, conversion on the first type is only
performed if the dtype of the input is not in the list.
order : {'F', 'C'} or None, default=None
Whether an array will be forced to be fortran or c-style.
When order is None (default), then if copy=False, nothing is ensured
about the memory layout of the output array; otherwise (copy=True)
the memory layout of the returned array is kept as close as possible
to the original array.
copy : bool, default=False
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : bool or 'allow-nan', default=True
Whether to raise an error on np.inf, np.nan, pd.NA in array. The
possibilities are:
- True: Force all values of array to be finite.
- False: accepts np.inf, np.nan, pd.NA in array.
- 'allow-nan': accepts only np.nan and pd.NA values in array. Values
cannot be infinite.
.. versionadded:: 0.20
``force_all_finite`` accepts the string ``'allow-nan'``.
.. versionchanged:: 0.23
Accepts `pd.NA` and converts it into `np.nan`
ensure_2d : bool, default=True
Whether to raise a value error if array is not 2D.
allow_nd : bool, default=False
Whether to allow array.ndim > 2.
ensure_min_samples : int, default=1
Make sure that the array has a minimum number of samples in its first
axis (rows for a 2D array). Setting to 0 disables this check.
ensure_min_features : int, default=1
Make sure that the 2D array has some minimum number of features
(columns). The default value of 1 rejects empty datasets.
This check is only enforced when the input data has effectively 2
dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
disables this check.
estimator : str or estimator instance, default=None
If passed, include the name of the estimator in warning messages.
input_name : str, default=""
The data name used to construct the error message. In particular
if `input_name` is "X" and the data has NaN values and
allow_nan is False, the error message will link to the imputer
documentation.
.. versionadded:: 1.1.0
Returns
-------
array_converted : object
The converted and validated array.
Examples
--------
>>> from sklearn.utils.validation import check_array
>>> X = [[1, 2, 3], [4, 5, 6]]
>>> X_checked = check_array(X)
>>> X_checked
array([[1, 2, 3], [4, 5, 6]])
"""
if isinstance(array, np.matrix):
raise TypeError(
"np.matrix is not supported. Please convert to a numpy array with "
"np.asarray. For more information see: "
"https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
)
xp, is_array_api_compliant = get_namespace(array)
# store reference to original array to check if copy is needed when
# function returns
array_orig = array
# store whether originally we wanted numeric dtype
dtype_numeric = isinstance(dtype, str) and dtype == "numeric"
dtype_orig = getattr(array, "dtype", None)
if not is_array_api_compliant and not hasattr(dtype_orig, "kind"):
# not a data type (e.g. a column named dtype in a pandas DataFrame)
dtype_orig = None
# check if the object contains several dtypes (typically a pandas
# DataFrame), and store them. If not, store None.
dtypes_orig = None
pandas_requires_conversion = False
# track if we have a Series-like object to raise a better error message
type_if_series = None
if hasattr(array, "dtypes") and hasattr(array.dtypes, "__array__"):
# throw warning if columns are sparse. If all columns are sparse, then
# array.sparse exists and sparsity will be preserved (later).
with suppress(ImportError):
from pandas import SparseDtype
def is_sparse(dtype):
return isinstance(dtype, SparseDtype)
if not hasattr(array, "sparse") and array.dtypes.apply(is_sparse).any():
warnings.warn(
"pandas.DataFrame with sparse columns found."
"It will be converted to a dense numpy array."
)
dtypes_orig = list(array.dtypes)
pandas_requires_conversion = any(
_pandas_dtype_needs_early_conversion(i) for i in dtypes_orig
)
if all(isinstance(dtype_iter, np.dtype) for dtype_iter in dtypes_orig):
dtype_orig = np.result_type(*dtypes_orig)
elif pandas_requires_conversion and any(d == object for d in dtypes_orig):
# Force object if any of the dtypes is an object
dtype_orig = object
elif (_is_extension_array_dtype(array) or hasattr(array, "iloc")) and hasattr(
array, "dtype"
):
# array is a pandas series
type_if_series = type(array)
pandas_requires_conversion = _pandas_dtype_needs_early_conversion(array.dtype)
if isinstance(array.dtype, np.dtype):
dtype_orig = array.dtype
else:
# Set to None to let array.astype work out the best dtype
dtype_orig = None
if dtype_numeric:
if (
dtype_orig is not None
and hasattr(dtype_orig, "kind")
and dtype_orig.kind == "O"
):
# if input is object, convert to float.
dtype = xp.float64
else:
dtype = None
if isinstance(dtype, (list, tuple)):
if dtype_orig is not None and dtype_orig in dtype:
# no dtype conversion required
dtype = None
else:
# dtype conversion required. Let's select the first element of the
# list of accepted types.
dtype = dtype[0]
if pandas_requires_conversion:
# pandas dataframe requires conversion earlier to handle extension dtypes with
# nans
# Use the original dtype for conversion if dtype is None
new_dtype = dtype_orig if dtype is None else dtype
array = array.astype(new_dtype)
# Since we converted here, we do not need to convert again later
dtype = None
if force_all_finite not in (True, False, "allow-nan"):
raise ValueError(
'force_all_finite should be a bool or "allow-nan". Got {!r} instead'.format(
force_all_finite
)
)
if d | (array, accept_sparse=False, *, accept_large_sparse=True, dtype='numeric', order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, estimator=None, input_name='') | [
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18,103 | sklearn.utils.multiclass | check_classification_targets | Ensure that target y is of a non-regression type.
Only the following target types (as defined in type_of_target) are allowed:
'binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences'
Parameters
----------
y : array-like
Target values.
| def check_classification_targets(y):
"""Ensure that target y is of a non-regression type.
Only the following target types (as defined in type_of_target) are allowed:
'binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences'
Parameters
----------
y : array-like
Target values.
"""
y_type = type_of_target(y, input_name="y")
if y_type not in [
"binary",
"multiclass",
"multiclass-multioutput",
"multilabel-indicator",
"multilabel-sequences",
]:
raise ValueError(
f"Unknown label type: {y_type}. Maybe you are trying to fit a "
"classifier, which expects discrete classes on a "
"regression target with continuous values."
)
| (y) | [
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] |
18,104 | imodels.util.arguments | check_fit_arguments | Process arguments for fit and predict methods.
| def check_fit_arguments(model, X, y, feature_names):
"""Process arguments for fit and predict methods.
"""
if isinstance(model, ClassifierMixin):
model.classes_, y = np.unique(y, return_inverse=True) # deals with str inputs
check_classification_targets(y)
if feature_names is None:
if isinstance(X, pd.DataFrame):
model.feature_names_ = X.columns
elif isinstance(X, list):
model.feature_names_ = ['X' + str(i) for i in range(len(X[0]))]
else:
model.feature_names_ = ['X' + str(i) for i in range(X.shape[1])]
else:
model.feature_names_ = feature_names
if scipy.sparse.issparse(X):
X = X.toarray()
X, y = check_X_y(X, y)
_, model.n_features_in_ = X.shape
assert len(model.feature_names_) == model.n_features_in_, 'feature_names should be same size as X.shape[1]'
y = y.astype(float)
return X, y, model.feature_names_
| (model, X, y, feature_names) | [
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] |
18,105 | sklearn.utils.validation | check_is_fitted | Perform is_fitted validation for estimator.
Checks if the estimator is fitted by verifying the presence of
fitted attributes (ending with a trailing underscore) and otherwise
raises a NotFittedError with the given message.
If an estimator does not set any attributes with a trailing underscore, it
can define a ``__sklearn_is_fitted__`` method returning a boolean to
specify if the estimator is fitted or not. See
:ref:`sphx_glr_auto_examples_developing_estimators_sklearn_is_fitted.py`
for an example on how to use the API.
Parameters
----------
estimator : estimator instance
Estimator instance for which the check is performed.
attributes : str, list or tuple of str, default=None
Attribute name(s) given as string or a list/tuple of strings
Eg.: ``["coef_", "estimator_", ...], "coef_"``
If `None`, `estimator` is considered fitted if there exist an
attribute that ends with a underscore and does not start with double
underscore.
msg : str, default=None
The default error message is, "This %(name)s instance is not fitted
yet. Call 'fit' with appropriate arguments before using this
estimator."
For custom messages if "%(name)s" is present in the message string,
it is substituted for the estimator name.
Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
all_or_any : callable, {all, any}, default=all
Specify whether all or any of the given attributes must exist.
Raises
------
TypeError
If the estimator is a class or not an estimator instance
NotFittedError
If the attributes are not found.
Examples
--------
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.utils.validation import check_is_fitted
>>> from sklearn.exceptions import NotFittedError
>>> lr = LogisticRegression()
>>> try:
... check_is_fitted(lr)
... except NotFittedError as exc:
... print(f"Model is not fitted yet.")
Model is not fitted yet.
>>> lr.fit([[1, 2], [1, 3]], [1, 0])
LogisticRegression()
>>> check_is_fitted(lr)
| def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all):
"""Perform is_fitted validation for estimator.
Checks if the estimator is fitted by verifying the presence of
fitted attributes (ending with a trailing underscore) and otherwise
raises a NotFittedError with the given message.
If an estimator does not set any attributes with a trailing underscore, it
can define a ``__sklearn_is_fitted__`` method returning a boolean to
specify if the estimator is fitted or not. See
:ref:`sphx_glr_auto_examples_developing_estimators_sklearn_is_fitted.py`
for an example on how to use the API.
Parameters
----------
estimator : estimator instance
Estimator instance for which the check is performed.
attributes : str, list or tuple of str, default=None
Attribute name(s) given as string or a list/tuple of strings
Eg.: ``["coef_", "estimator_", ...], "coef_"``
If `None`, `estimator` is considered fitted if there exist an
attribute that ends with a underscore and does not start with double
underscore.
msg : str, default=None
The default error message is, "This %(name)s instance is not fitted
yet. Call 'fit' with appropriate arguments before using this
estimator."
For custom messages if "%(name)s" is present in the message string,
it is substituted for the estimator name.
Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
all_or_any : callable, {all, any}, default=all
Specify whether all or any of the given attributes must exist.
Raises
------
TypeError
If the estimator is a class or not an estimator instance
NotFittedError
If the attributes are not found.
Examples
--------
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.utils.validation import check_is_fitted
>>> from sklearn.exceptions import NotFittedError
>>> lr = LogisticRegression()
>>> try:
... check_is_fitted(lr)
... except NotFittedError as exc:
... print(f"Model is not fitted yet.")
Model is not fitted yet.
>>> lr.fit([[1, 2], [1, 3]], [1, 0])
LogisticRegression()
>>> check_is_fitted(lr)
"""
if isclass(estimator):
raise TypeError("{} is a class, not an instance.".format(estimator))
if msg is None:
msg = (
"This %(name)s instance is not fitted yet. Call 'fit' with "
"appropriate arguments before using this estimator."
)
if not hasattr(estimator, "fit"):
raise TypeError("%s is not an estimator instance." % (estimator))
if not _is_fitted(estimator, attributes, all_or_any):
raise NotFittedError(msg % {"name": type(estimator).__name__})
| (estimator, attributes=None, *, msg=None, all_or_any=<built-in function all>) | [
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] |
18,106 | copy | deepcopy | Deep copy operation on arbitrary Python objects.
See the module's __doc__ string for more info.
| def deepcopy(x, memo=None, _nil=[]):
"""Deep copy operation on arbitrary Python objects.
See the module's __doc__ string for more info.
"""
if memo is None:
memo = {}
d = id(x)
y = memo.get(d, _nil)
if y is not _nil:
return y
cls = type(x)
copier = _deepcopy_dispatch.get(cls)
if copier is not None:
y = copier(x, memo)
else:
if issubclass(cls, type):
y = _deepcopy_atomic(x, memo)
else:
copier = getattr(x, "__deepcopy__", None)
if copier is not None:
y = copier(memo)
else:
reductor = dispatch_table.get(cls)
if reductor:
rv = reductor(x)
else:
reductor = getattr(x, "__reduce_ex__", None)
if reductor is not None:
rv = reductor(4)
else:
reductor = getattr(x, "__reduce__", None)
if reductor:
rv = reductor()
else:
raise Error(
"un(deep)copyable object of type %s" % cls)
if isinstance(rv, str):
y = x
else:
y = _reconstruct(x, memo, *rv)
# If is its own copy, don't memoize.
if y is not x:
memo[d] = y
_keep_alive(x, memo) # Make sure x lives at least as long as d
return y
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18,107 | imodels.util.convert | dict_to_rule |
Function to accept rule dict and convert to Rule object
Parameters:
rule: list of dict of schema
[
{
'feature': int,
'operator': str,
'value': float
},
]
| def dict_to_rule(rule, clf_feature_dict):
"""
Function to accept rule dict and convert to Rule object
Parameters:
rule: list of dict of schema
[
{
'feature': int,
'operator': str,
'value': float
},
]
"""
output = ''
for condition in rule:
output += '{} {} {} and '.format(
clf_feature_dict[int(condition['feature'])],
condition['operator'],
condition['pivot']
)
return output[:-5]
| (rule, clf_feature_dict) | [
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18,110 | imodels.util.explain_errors | explain_classification_errors | Explains the classification errors of a model by fitting an interpretable model to them.
Currently only supports binary classification.
Parameters
----------
X: array_like
(n, n_features)
predictions: array_like
(n, 1) predictions
y
(n, 1) targets with integer values representing class
feature_names
n_features
Returns
-------
model: BaseEstimator
| def explain_classification_errors(X, predictions, y,
feature_names: list = None,
target_name: str = None,
classifier: BaseEstimator = imodels.GreedyTreeClassifier(),
target_one_hot_encode: bool = False,
print_rules: bool = True):
"""Explains the classification errors of a model by fitting an interpretable model to them.
Currently only supports binary classification.
Parameters
----------
X: array_like
(n, n_features)
predictions: array_like
(n, 1) predictions
y
(n, 1) targets with integer values representing class
feature_names
n_features
Returns
-------
model: BaseEstimator
"""
# deal with names
if feature_names is None:
if isinstance(X, pd.DataFrame):
feature_names = X.columns.tolist()
else:
feature_names = [f'X{i + 1}' for i in range(X.shape[1])]
if target_name is None:
if isinstance(y, pd.DataFrame):
target_name = y.columns[0]
elif isinstance(y, pd.Series):
target_name = y.name
else:
target_name = 'target'
if isinstance(predictions, pd.Series) or isinstance(predictions, pd.DataFrame):
predictions = predictions.values
X, y = check_X_y(X, y) # converts to np
if len(y.shape) == 1:
y = y.reshape(-1, 1)
if len(predictions.shape) == 1:
predictions = predictions.reshape(-1, 1)
errors = np.array(predictions != y).astype(int)
features = pd.DataFrame(np.hstack((X, y)))
features.columns = [*feature_names, target_name]
classifier.fit(features, errors.flatten())
if print_rules:
print(classifier)
return classifier, features.columns
| (X, predictions, y, feature_names: Optional[list] = None, target_name: Optional[str] = None, classifier: sklearn.base.BaseEstimator = GreedyTreeClassifier(), target_one_hot_encode: bool = False, print_rules: bool = True) | [
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] |
18,111 | imodels.util.data_util | get_clean_dataset | Fetch clean data (as numpy arrays) from various sources including imodels, pmlb, openml, and sklearn.
If data is not downloaded, will download and cache. Otherwise will load locally.
Cleans features so that they are type float and features names don't start with a digit.
Parameters
----------
dataset_name: str
Checks for unique identifier in imodels.util.data_util.DSET_KWARGS
Otherwise, unique dataset identifier (see https://github.com/csinva/imodels-data for unique identifiers)
data_source: str
options: 'imodels', 'pmlb', 'sklearn', 'openml', 'synthetic'
data_path: str
path to load/save data (default: 'data')
test_size: float, optional
if not None, will split data into train and test sets (with fraction test_size in test set)
& change the return signature to `X_train, X_test, y_train, y_test, feature_names`
random_state: int, optional
if test_size is not None, will use this random state to split data
Returns
-------
X: np.ndarray
features
y: np.ndarray
outcome
feature_names: list
Example
-------
```
# download compas dataset from imodels
X, y, feature_names = imodels.get_clean_dataset('compas_two_year_clean', data_source='imodels')
# download ionosphere dataset from pmlb
X, y, feature_names = imodels.get_clean_dataset('ionosphere', data_source='pmlb')
# download liver dataset from openml
X, y, feature_names = imodels.get_clean_dataset('8', data_source='openml')
# download ca housing from sklearn
X, y, feature_names = imodels.get_clean_dataset('california_housing', data_source='sklearn')
```
| def get_clean_dataset(
dataset_name: str,
data_source: str = "imodels",
data_path=os.path.expanduser("~/cache_imodels_data"),
convertna: bool = True,
test_size: float = None,
random_state: int = 42,
verbose=True,
) -> Tuple[np.ndarray, np.ndarray, list]:
"""Fetch clean data (as numpy arrays) from various sources including imodels, pmlb, openml, and sklearn.
If data is not downloaded, will download and cache. Otherwise will load locally.
Cleans features so that they are type float and features names don't start with a digit.
Parameters
----------
dataset_name: str
Checks for unique identifier in imodels.util.data_util.DSET_KWARGS
Otherwise, unique dataset identifier (see https://github.com/csinva/imodels-data for unique identifiers)
data_source: str
options: 'imodels', 'pmlb', 'sklearn', 'openml', 'synthetic'
data_path: str
path to load/save data (default: 'data')
test_size: float, optional
if not None, will split data into train and test sets (with fraction test_size in test set)
& change the return signature to `X_train, X_test, y_train, y_test, feature_names`
random_state: int, optional
if test_size is not None, will use this random state to split data
Returns
-------
X: np.ndarray
features
y: np.ndarray
outcome
feature_names: list
Example
-------
```
# download compas dataset from imodels
X, y, feature_names = imodels.get_clean_dataset('compas_two_year_clean', data_source='imodels')
# download ionosphere dataset from pmlb
X, y, feature_names = imodels.get_clean_dataset('ionosphere', data_source='pmlb')
# download liver dataset from openml
X, y, feature_names = imodels.get_clean_dataset('8', data_source='openml')
# download ca housing from sklearn
X, y, feature_names = imodels.get_clean_dataset('california_housing', data_source='sklearn')
```
"""
if dataset_name in DSET_KWARGS:
if verbose:
data_source = DSET_KWARGS[dataset_name]["data_source"]
dataset_name = DSET_KWARGS[dataset_name]["dataset_name"]
print(f"fetching {dataset_name} from {data_source}")
assert data_source in ["imodels", "pmlb", "imodels-multitask", "sklearn", "openml", "synthetic"], (
data_source + " not correct"
)
if test_size is not None:
def _split(X, y, feature_names):
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=test_size, random_state=random_state
)
return X_train, X_test, y_train, y_test, feature_names
else:
def _split(X, y, feature_names):
return X, y, feature_names
if data_source == "imodels":
if not dataset_name.endswith("csv"):
dataset_name = dataset_name + ".csv"
if not os.path.isfile(dataset_name):
_download_imodels_dataset(dataset_name, data_path)
df = pd.read_csv(oj(data_path, "imodels_data", dataset_name))
X, y = df.iloc[:, :-1].values, df.iloc[:, -1].values
feature_names = df.columns.values[:-1]
if convertna:
X = np.nan_to_num(X.astype("float32"))
return _split(X, y, _clean_feat_names(feature_names))
elif data_source == 'imodels-multitask':
if not dataset_name.endswith("csv"):
dataset_name = dataset_name + ".csv"
if not os.path.isfile(dataset_name):
_download_imodels_multitask_dataset(dataset_name, data_path)
df = pd.read_csv(oj(data_path, "imodels_multitask_data", dataset_name))
target_cols = [col for col in df.columns if col.endswith('__target')]
feature_names = [col for col in df.columns if col not in target_cols]
X, y = df[feature_names].values, df[target_cols].values
if convertna:
X = np.nan_to_num(X.astype("float32"))
return _split(X, y, _clean_feat_names(feature_names))
elif data_source == "pmlb":
from pmlb import fetch_data
feature_names = list(
fetch_data(
dataset_name,
return_X_y=False,
local_cache_dir=oj(data_path, "pmlb_data"),
).columns
)
feature_names.remove("target")
X, y = fetch_data(
dataset_name, return_X_y=True, local_cache_dir=oj(data_path, "pmlb_data")
)
if (
np.unique(y).size == 2
): # if binary classification, ensure that the classes are 0 and 1
y -= np.min(y)
return _split(_clean_features(X), y, _clean_feat_names(feature_names))
elif data_source == "sklearn":
if dataset_name == "diabetes":
data = sklearn.datasets.load_diabetes()
elif dataset_name == "california_housing":
data = sklearn.datasets.fetch_california_housing(
data_home=oj(data_path, "sklearn_data")
)
elif dataset_name == "breast_cancer":
data = sklearn.datasets.load_breast_cancer()
return data["data"], data["target"], _clean_feat_names(data["feature_names"])
elif (
data_source == "openml"
): # note this api might change in newer sklearn - should give dataset-id not name
data = sklearn.datasets.fetch_openml(
data_id=dataset_name, data_home=oj(data_path, "openml_data"), parser="auto"
)
X, y, feature_names = (
data["data"],
data["target"],
_clean_feat_names(data["feature_names"]),
)
if isinstance(X, pd.DataFrame):
X = X.values
if isinstance(y, pd.Series):
y = y.values
y = _define_openml_outcomes(y, dataset_name)
return _split(_clean_features(X), y, _clean_feat_names(feature_names))
elif data_source == "synthetic":
if dataset_name == "friedman1":
X, y = sklearn.datasets.make_friedman1(
n_samples=200, n_features=10)
elif dataset_name == "friedman2":
X, y = sklearn.datasets.make_friedman2(n_samples=200)
elif dataset_name == "friedman3":
X, y = sklearn.datasets.make_friedman3(n_samples=200)
elif dataset_name == "radchenko_james":
X, y = make_rj()
elif dataset_name == "vo_pati":
X, y = make_vp()
return _split(X, y, ["X_" + str(i + 1) for i in range(X.shape[1])])
| (dataset_name: str, data_source: str = 'imodels', data_path='/root/cache_imodels_data', convertna: bool = True, test_size: Optional[float] = None, random_state: int = 42, verbose=True) -> Tuple[numpy.ndarray, numpy.ndarray, list] | [
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18,112 | imodels.util.rule | get_feature_dict | null | def get_feature_dict(num_features: int, feature_names: Iterable[str] = None) -> Dict[str, str]:
feature_dict = OrderedDict()
if feature_names is not None:
for i in range(num_features):
feature_dict[f'X_{i}'] = feature_names[i]
else:
for i in range(num_features):
feature_dict[f'X_{i}'] = f'X_{i}'
return feature_dict
| (num_features: int, feature_names: Optional[Iterable[str]] = None) -> Dict[str, str] | [
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18,113 | sklearn.preprocessing._data | normalize | Scale input vectors individually to unit norm (vector length).
Read more in the :ref:`User Guide <preprocessing_normalization>`.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The data to normalize, element by element.
scipy.sparse matrices should be in CSR format to avoid an
un-necessary copy.
norm : {'l1', 'l2', 'max'}, default='l2'
The norm to use to normalize each non zero sample (or each non-zero
feature if axis is 0).
axis : {0, 1}, default=1
Define axis used to normalize the data along. If 1, independently
normalize each sample, otherwise (if 0) normalize each feature.
copy : bool, default=True
If False, try to avoid a copy and normalize in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.
return_norm : bool, default=False
Whether to return the computed norms.
Returns
-------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
Normalized input X.
norms : ndarray of shape (n_samples, ) if axis=1 else (n_features, )
An array of norms along given axis for X.
When X is sparse, a NotImplementedError will be raised
for norm 'l1' or 'l2'.
See Also
--------
Normalizer : Performs normalization using the Transformer API
(e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`).
Notes
-----
For a comparison of the different scalers, transformers, and normalizers,
see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`.
Examples
--------
>>> from sklearn.preprocessing import normalize
>>> X = [[-2, 1, 2], [-1, 0, 1]]
>>> normalize(X, norm="l1") # L1 normalization each row independently
array([[-0.4, 0.2, 0.4],
[-0.5, 0. , 0.5]])
>>> normalize(X, norm="l2") # L2 normalization each row independently
array([[-0.66..., 0.33..., 0.66...],
[-0.70..., 0. , 0.70...]])
| def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True):
"""Standardize a dataset along any axis.
Center to the mean and component wise scale to unit variance.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The data to center and scale.
axis : {0, 1}, default=0
Axis used to compute the means and standard deviations along. If 0,
independently standardize each feature, otherwise (if 1) standardize
each sample.
with_mean : bool, default=True
If True, center the data before scaling.
with_std : bool, default=True
If True, scale the data to unit variance (or equivalently,
unit standard deviation).
copy : bool, default=True
If False, try to avoid a copy and scale in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.
Returns
-------
X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)
The transformed data.
See Also
--------
StandardScaler : Performs scaling to unit variance using the Transformer
API (e.g. as part of a preprocessing
:class:`~sklearn.pipeline.Pipeline`).
Notes
-----
This implementation will refuse to center scipy.sparse matrices
since it would make them non-sparse and would potentially crash the
program with memory exhaustion problems.
Instead the caller is expected to either set explicitly
`with_mean=False` (in that case, only variance scaling will be
performed on the features of the CSC matrix) or to call `X.toarray()`
if he/she expects the materialized dense array to fit in memory.
To avoid memory copy the caller should pass a CSC matrix.
NaNs are treated as missing values: disregarded to compute the statistics,
and maintained during the data transformation.
We use a biased estimator for the standard deviation, equivalent to
`numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to
affect model performance.
For a comparison of the different scalers, transformers, and normalizers,
see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`.
.. warning:: Risk of data leak
Do not use :func:`~sklearn.preprocessing.scale` unless you know
what you are doing. A common mistake is to apply it to the entire data
*before* splitting into training and test sets. This will bias the
model evaluation because information would have leaked from the test
set to the training set.
In general, we recommend using
:class:`~sklearn.preprocessing.StandardScaler` within a
:ref:`Pipeline <pipeline>` in order to prevent most risks of data
leaking: `pipe = make_pipeline(StandardScaler(), LogisticRegression())`.
Examples
--------
>>> from sklearn.preprocessing import scale
>>> X = [[-2, 1, 2], [-1, 0, 1]]
>>> scale(X, axis=0) # scaling each column independently
array([[-1., 1., 1.],
[ 1., -1., -1.]])
>>> scale(X, axis=1) # scaling each row independently
array([[-1.37..., 0.39..., 0.98...],
[-1.22..., 0. , 1.22...]])
"""
X = check_array(
X,
accept_sparse="csc",
copy=copy,
ensure_2d=False,
estimator="the scale function",
dtype=FLOAT_DTYPES,
force_all_finite="allow-nan",
)
if sparse.issparse(X):
if with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` instead"
" See docstring for motivation and alternatives."
)
if axis != 0:
raise ValueError(
"Can only scale sparse matrix on axis=0, got axis=%d" % axis
)
if with_std:
_, var = mean_variance_axis(X, axis=0)
var = _handle_zeros_in_scale(var, copy=False)
inplace_column_scale(X, 1 / np.sqrt(var))
else:
X = np.asarray(X)
if with_mean:
mean_ = np.nanmean(X, axis)
if with_std:
scale_ = np.nanstd(X, axis)
# Xr is a view on the original array that enables easy use of
# broadcasting on the axis in which we are interested in
Xr = np.rollaxis(X, axis)
if with_mean:
Xr -= mean_
mean_1 = np.nanmean(Xr, axis=0)
# Verify that mean_1 is 'close to zero'. If X contains very
# large values, mean_1 can also be very large, due to a lack of
# precision of mean_. In this case, a pre-scaling of the
# concerned feature is efficient, for instance by its mean or
# maximum.
if not np.allclose(mean_1, 0):
warnings.warn(
"Numerical issues were encountered "
"when centering the data "
"and might not be solved. Dataset may "
"contain too large values. You may need "
"to prescale your features."
)
Xr -= mean_1
if with_std:
scale_ = _handle_zeros_in_scale(scale_, copy=False)
Xr /= scale_
if with_mean:
mean_2 = np.nanmean(Xr, axis=0)
# If mean_2 is not 'close to zero', it comes from the fact that
# scale_ is very small so that mean_2 = mean_1/scale_ > 0, even
# if mean_1 was close to zero. The problem is thus essentially
# due to the lack of precision of mean_. A solution is then to
# subtract the mean again:
if not np.allclose(mean_2, 0):
warnings.warn(
"Numerical issues were encountered "
"when scaling the data "
"and might not be solved. The standard "
"deviation of the data is probably "
"very close to 0. "
)
Xr -= mean_2
return X
| (X, norm='l2', *, axis=1, copy=True, return_norm=False) | [
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18,116 | imodels.util.rule | replace_feature_name | null | def replace_feature_name(rule: Rule, replace_dict: Dict[str, str]) -> Rule:
def replace(match):
return replace_dict[match.group(0)]
rule_replaced = copy.copy(rule)
rule_replaced.rule = re.sub('|'.join(r'\b%s\b' % re.escape(s) for s in replace_dict), replace, rule.rule)
replaced_agg_dict = {}
for feature, symbol in rule_replaced.agg_dict:
replaced_agg_dict[(replace_dict[feature], symbol)] = rule_replaced.agg_dict[(feature, symbol)]
rule_replaced.agg_dict = replaced_agg_dict
return rule_replaced
| (rule: imodels.util.rule.Rule, replace_dict: Dict[str, str]) -> imodels.util.rule.Rule | [
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18,120 | sklearn.model_selection._split | train_test_split | Split arrays or matrices into random train and test subsets.
Quick utility that wraps input validation,
``next(ShuffleSplit().split(X, y))``, and application to input data
into a single call for splitting (and optionally subsampling) data into a
one-liner.
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.
test_size : float or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion
of the dataset to include in the test split. If int, represents the
absolute number of test samples. If None, the value is set to the
complement of the train size. If ``train_size`` is also None, it will
be set to 0.25.
train_size : float or int, default=None
If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.
random_state : int, RandomState instance or None, default=None
Controls the shuffling applied to the data before applying the split.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
shuffle : bool, default=True
Whether or not to shuffle the data before splitting. If shuffle=False
then stratify must be None.
stratify : array-like, default=None
If not None, data is split in a stratified fashion, using this as
the class labels.
Read more in the :ref:`User Guide <stratification>`.
Returns
-------
splitting : list, length=2 * len(arrays)
List containing train-test split of inputs.
.. versionadded:: 0.16
If the input is sparse, the output will be a
``scipy.sparse.csr_matrix``. Else, output type is the same as the
input type.
Examples
--------
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
[0, 1],
[6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
[8, 9]])
>>> y_test
[1, 4]
>>> train_test_split(y, shuffle=False)
[[0, 1, 2], [3, 4]]
| def __repr__(self):
return _build_repr(self)
| (*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None) | [
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] |
18,122 | imodels.util.convert | tree_to_code | Prints a tree with a single split
| def tree_to_code(clf, feature_names):
'''Prints a tree with a single split
'''
n_nodes = clf.tree_.node_count
children_left = clf.tree_.children_left
children_right = clf.tree_.children_right
feature = clf.tree_.feature
threshold = clf.tree_.threshold
node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
is_leaves = np.zeros(shape=n_nodes, dtype=bool)
stack = [(0, 0)] # start with the root node id (0) and its depth (0)
s = ''
while len(stack) > 0:
# `pop` ensures each node is only visited once
node_id, depth = stack.pop()
node_depth[node_id] = depth
# If the left and right child of a node is not the same we have a split
# node
is_split_node = children_left[node_id] != children_right[node_id]
# If a split node, append left and right children and depth to `stack`
# so we can loop through them
if is_split_node:
stack.append((children_left[node_id], depth + 1))
stack.append((children_right[node_id], depth + 1))
else:
is_leaves[node_id] = True
# print("The binary tree structure has {n} nodes and has "
# "the following tree structure:\n".format(n=n_nodes))
for i in range(n_nodes):
if is_leaves[i]:
pass
# print("{space}node={node} is a leaf node.".format(
# space=node_depth[i] * "\t", node=i))
else:
s += f"{feature_names[feature[i]]} <= {threshold[i]}"
return f"\033[96m{s}\033[00m\n"
| (clf, feature_names) | [
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18,123 | imodels.util.convert | tree_to_rules |
Return a list of rules from a tree
Parameters
----------
tree : Decision Tree Classifier/Regressor
feature_names: list of variable names
Returns
-------
rules : list of rules.
| def tree_to_rules(tree: Union[DecisionTreeClassifier, DecisionTreeRegressor],
feature_names: List[str],
prediction_values: bool = False, round_thresholds=True) -> List[str]:
"""
Return a list of rules from a tree
Parameters
----------
tree : Decision Tree Classifier/Regressor
feature_names: list of variable names
Returns
-------
rules : list of rules.
"""
# XXX todo: check the case where tree is build on subset of features,
# ie max_features != None
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
rules = []
def recurse(node, base_name):
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
symbol = '<='
symbol2 = '>'
threshold = tree_.threshold[node]
if round_thresholds:
threshold = np.round(threshold, decimals=5)
text = base_name + ["{} {} {}".format(name, symbol, threshold)]
recurse(tree_.children_left[node], text)
text = base_name + ["{} {} {}".format(name, symbol2,
threshold)]
recurse(tree_.children_right[node], text)
else:
rule = str.join(' and ', base_name)
rule = (rule if rule != ''
else ' == '.join([feature_names[0]] * 2))
# a rule selecting all is set to "c0==c0"
if prediction_values:
rules.append((rule, tree_.value[node][0].tolist()))
else:
rules.append(rule)
recurse(0, [])
return rules if len(rules) > 0 else 'True'
| (tree: Union[sklearn.tree._classes.DecisionTreeClassifier, sklearn.tree._classes.DecisionTreeRegressor], feature_names: List[str], prediction_values: bool = False, round_thresholds=True) -> List[str] | [
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] |
18,124 | sklearn.utils.multiclass | unique_labels | Extract an ordered array of unique labels.
We don't allow:
- mix of multilabel and multiclass (single label) targets
- mix of label indicator matrix and anything else,
because there are no explicit labels)
- mix of label indicator matrices of different sizes
- mix of string and integer labels
At the moment, we also don't allow "multiclass-multioutput" input type.
Parameters
----------
*ys : array-likes
Label values.
Returns
-------
out : ndarray of shape (n_unique_labels,)
An ordered array of unique labels.
Examples
--------
>>> from sklearn.utils.multiclass import unique_labels
>>> unique_labels([3, 5, 5, 5, 7, 7])
array([3, 5, 7])
>>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4])
array([1, 2, 3, 4])
>>> unique_labels([1, 2, 10], [5, 11])
array([ 1, 2, 5, 10, 11])
| def unique_labels(*ys):
"""Extract an ordered array of unique labels.
We don't allow:
- mix of multilabel and multiclass (single label) targets
- mix of label indicator matrix and anything else,
because there are no explicit labels)
- mix of label indicator matrices of different sizes
- mix of string and integer labels
At the moment, we also don't allow "multiclass-multioutput" input type.
Parameters
----------
*ys : array-likes
Label values.
Returns
-------
out : ndarray of shape (n_unique_labels,)
An ordered array of unique labels.
Examples
--------
>>> from sklearn.utils.multiclass import unique_labels
>>> unique_labels([3, 5, 5, 5, 7, 7])
array([3, 5, 7])
>>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4])
array([1, 2, 3, 4])
>>> unique_labels([1, 2, 10], [5, 11])
array([ 1, 2, 5, 10, 11])
"""
xp, is_array_api_compliant = get_namespace(*ys)
if not ys:
raise ValueError("No argument has been passed.")
# Check that we don't mix label format
ys_types = set(type_of_target(x) for x in ys)
if ys_types == {"binary", "multiclass"}:
ys_types = {"multiclass"}
if len(ys_types) > 1:
raise ValueError("Mix type of y not allowed, got types %s" % ys_types)
label_type = ys_types.pop()
# Check consistency for the indicator format
if (
label_type == "multilabel-indicator"
and len(
set(
check_array(y, accept_sparse=["csr", "csc", "coo"]).shape[1] for y in ys
)
)
> 1
):
raise ValueError(
"Multi-label binary indicator input with different numbers of labels"
)
# Get the unique set of labels
_unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
if not _unique_labels:
raise ValueError("Unknown label type: %s" % repr(ys))
if is_array_api_compliant:
# array_api does not allow for mixed dtypes
unique_ys = xp.concat([_unique_labels(y) for y in ys])
return xp.unique_values(unique_ys)
ys_labels = set(chain.from_iterable((i for i in _unique_labels(y)) for y in ys))
# Check that we don't mix string type with number type
if len(set(isinstance(label, str) for label in ys_labels)) > 1:
raise ValueError("Mix of label input types (string and number)")
return xp.asarray(sorted(ys_labels))
| (*ys) | [
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18,126 | jsondiff | CompactJsonDiffSyntax | null | class CompactJsonDiffSyntax(object):
def emit_set_diff(self, a, b, s, added, removed):
if s == 0.0 or len(removed) == len(a):
return {replace: b} if isinstance(b, dict) else b
else:
d = {}
if removed:
d[discard] = removed
if added:
d[add] = added
return d
def emit_list_diff(self, a, b, s, inserted, changed, deleted):
if s == 0.0:
return {replace: b} if isinstance(b, dict) else b
elif s == 1.0:
return {}
else:
d = changed
if inserted:
d[insert] = inserted
if deleted:
d[delete] = [pos for pos, value in deleted]
return d
def emit_dict_diff(self, a, b, s, added, changed, removed):
if s == 0.0:
return {replace: b} if isinstance(b, dict) else b
elif s == 1.0:
return {}
else:
changed.update(added)
if removed:
changed[delete] = list(removed.keys())
return changed
def emit_value_diff(self, a, b, s):
if s == 1.0:
return {}
else:
return {replace: b} if isinstance(b, dict) else b
def patch(self, a, d):
if isinstance(d, dict):
if not d:
return a
if replace in d:
return d[replace]
if isinstance(a, dict):
a = dict(a)
for k, v in d.items():
if k is delete:
for kdel in v:
del a[kdel]
else:
av = a.get(k, missing)
if av is missing:
a[k] = v
else:
a[k] = self.patch(av, v)
return a
elif isinstance(a, (list, tuple)):
original_type = type(a)
a = list(a)
if delete in d:
for pos in d[delete]:
a.pop(pos)
if insert in d:
for pos, value in d[insert]:
a.insert(pos, value)
for k, v in d.items():
if k is not delete and k is not insert:
k = int(k)
a[k] = self.patch(a[k], v)
if original_type is not list:
a = original_type(a)
return a
elif isinstance(a, set):
a = set(a)
if discard in d:
for x in d[discard]:
a.discard(x)
if add in d:
for x in d[add]:
a.add(x)
return a
return d
| () | [
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18,127 | jsondiff | emit_dict_diff | null | def emit_dict_diff(self, a, b, s, added, changed, removed):
if s == 0.0:
return {replace: b} if isinstance(b, dict) else b
elif s == 1.0:
return {}
else:
changed.update(added)
if removed:
changed[delete] = list(removed.keys())
return changed
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18,128 | jsondiff | emit_list_diff | null | def emit_list_diff(self, a, b, s, inserted, changed, deleted):
if s == 0.0:
return {replace: b} if isinstance(b, dict) else b
elif s == 1.0:
return {}
else:
d = changed
if inserted:
d[insert] = inserted
if deleted:
d[delete] = [pos for pos, value in deleted]
return d
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18,129 | jsondiff | emit_set_diff | null | def emit_set_diff(self, a, b, s, added, removed):
if s == 0.0 or len(removed) == len(a):
return {replace: b} if isinstance(b, dict) else b
else:
d = {}
if removed:
d[discard] = removed
if added:
d[add] = added
return d
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18,130 | jsondiff | emit_value_diff | null | def emit_value_diff(self, a, b, s):
if s == 1.0:
return {}
else:
return {replace: b} if isinstance(b, dict) else b
| (self, a, b, s) | [
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18,131 | jsondiff | patch | null | def patch(self, a, d):
if isinstance(d, dict):
if not d:
return a
if replace in d:
return d[replace]
if isinstance(a, dict):
a = dict(a)
for k, v in d.items():
if k is delete:
for kdel in v:
del a[kdel]
else:
av = a.get(k, missing)
if av is missing:
a[k] = v
else:
a[k] = self.patch(av, v)
return a
elif isinstance(a, (list, tuple)):
original_type = type(a)
a = list(a)
if delete in d:
for pos in d[delete]:
a.pop(pos)
if insert in d:
for pos, value in d[insert]:
a.insert(pos, value)
for k, v in d.items():
if k is not delete and k is not insert:
k = int(k)
a[k] = self.patch(a[k], v)
if original_type is not list:
a = original_type(a)
return a
elif isinstance(a, set):
a = set(a)
if discard in d:
for x in d[discard]:
a.discard(x)
if add in d:
for x in d[add]:
a.add(x)
return a
return d
| (self, a, d) | [
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] |
18,132 | jsondiff | ExplicitJsonDiffSyntax | null | class ExplicitJsonDiffSyntax(object):
def emit_set_diff(self, a, b, s, added, removed):
if s == 0.0 or len(removed) == len(a):
return b
else:
d = {}
if removed:
d[discard] = removed
if added:
d[add] = added
return d
def emit_list_diff(self, a, b, s, inserted, changed, deleted):
if s == 0.0:
return b
elif s == 1.0:
return {}
else:
d = changed
if inserted:
d[insert] = inserted
if deleted:
d[delete] = [pos for pos, value in deleted]
return d
def emit_dict_diff(self, a, b, s, added, changed, removed):
if s == 0.0:
return b
elif s == 1.0:
return {}
else:
d = {}
if added:
d[insert] = added
if changed:
d[update] = changed
if removed:
d[delete] = list(removed.keys())
return d
def emit_value_diff(self, a, b, s):
if s == 1.0:
return {}
else:
return b
| () | [
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18,133 | jsondiff | emit_dict_diff | null | def emit_dict_diff(self, a, b, s, added, changed, removed):
if s == 0.0:
return b
elif s == 1.0:
return {}
else:
d = {}
if added:
d[insert] = added
if changed:
d[update] = changed
if removed:
d[delete] = list(removed.keys())
return d
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18,134 | jsondiff | emit_list_diff | null | def emit_list_diff(self, a, b, s, inserted, changed, deleted):
if s == 0.0:
return b
elif s == 1.0:
return {}
else:
d = changed
if inserted:
d[insert] = inserted
if deleted:
d[delete] = [pos for pos, value in deleted]
return d
| (self, a, b, s, inserted, changed, deleted) | [
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18,135 | jsondiff | emit_set_diff | null | def emit_set_diff(self, a, b, s, added, removed):
if s == 0.0 or len(removed) == len(a):
return b
else:
d = {}
if removed:
d[discard] = removed
if added:
d[add] = added
return d
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18,136 | jsondiff | emit_value_diff | null | def emit_value_diff(self, a, b, s):
if s == 1.0:
return {}
else:
return b
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18,137 | jsondiff | JsonDiffSyntax | null | class JsonDiffSyntax(object):
def emit_set_diff(self, a, b, s, added, removed):
raise NotImplementedError()
def emit_list_diff(self, a, b, s, inserted, changed, deleted):
raise NotImplementedError()
def emit_dict_diff(self, a, b, s, added, changed, removed):
raise NotImplementedError()
def emit_value_diff(self, a, b, s):
raise NotImplementedError()
def patch(self, a, d):
raise NotImplementedError()
def unpatch(self, a, d):
raise NotImplementedError()
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18,138 | jsondiff | emit_dict_diff | null | def emit_dict_diff(self, a, b, s, added, changed, removed):
raise NotImplementedError()
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18,139 | jsondiff | emit_list_diff | null | def emit_list_diff(self, a, b, s, inserted, changed, deleted):
raise NotImplementedError()
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18,140 | jsondiff | emit_set_diff | null | def emit_set_diff(self, a, b, s, added, removed):
raise NotImplementedError()
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18,141 | jsondiff | emit_value_diff | null | def emit_value_diff(self, a, b, s):
raise NotImplementedError()
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18,142 | jsondiff | patch | null | def patch(self, a, d):
raise NotImplementedError()
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18,143 | jsondiff | unpatch | null | def unpatch(self, a, d):
raise NotImplementedError()
| (self, a, d) | [
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18,144 | jsondiff | JsonDiffer | null | class JsonDiffer(object):
class Options(object):
pass
def __init__(self, syntax='compact', load=False, dump=False, marshal=False,
loader=default_loader, dumper=default_dumper, escape_str='$'):
self.options = JsonDiffer.Options()
self.options.syntax = builtin_syntaxes.get(syntax, syntax)
self.options.load = load
self.options.dump = dump
self.options.marshal = marshal
self.options.loader = loader
self.options.dumper = dumper
self.options.escape_str = escape_str
self._symbol_map = {
escape_str + symbol.label: symbol
for symbol in _all_symbols_
}
def _list_diff_0(self, C, X, Y):
i, j = len(X), len(Y)
r = []
while True:
if i > 0 and j > 0:
d, s = self._obj_diff(X[i-1], Y[j-1])
if s > 0 and C[i][j] == C[i-1][j-1] + s:
r.append((0, d, j-1, s))
i, j = i - 1, j - 1
continue
if j > 0 and (i == 0 or C[i][j-1] >= C[i-1][j]):
r.append((1, Y[j-1], j-1, 0.0))
j = j - 1
continue
if i > 0 and (j == 0 or C[i][j-1] < C[i-1][j]):
r.append((-1, X[i-1], i-1, 0.0))
i = i - 1
continue
return reversed(r)
def _list_diff(self, X, Y):
# LCS
m = len(X)
n = len(Y)
# An (m+1) times (n+1) matrix
C = [[0 for j in range(n+1)] for i in range(m+1)]
for i in range(1, m+1):
for j in range(1, n+1):
_, s = self._obj_diff(X[i-1], Y[j-1])
# Following lines are part of the original LCS algorithm
# left in the code in case modification turns out to be problematic
#if X[i-1] == Y[j-1]:
# C[i][j] = C[i-1][j-1] + 1
#else:
C[i][j] = max(C[i][j-1], C[i-1][j], C[i-1][j-1] + s)
inserted = []
deleted = []
changed = {}
tot_s = 0.0
for sign, value, pos, s in self._list_diff_0(C, X, Y):
if sign == 1:
inserted.append((pos, value))
elif sign == -1:
deleted.insert(0, (pos, value))
elif sign == 0 and s < 1:
changed[pos] = value
tot_s += s
tot_n = len(X) + len(inserted)
if tot_n == 0:
s = 1.0
else:
s = tot_s / tot_n
return self.options.syntax.emit_list_diff(X, Y, s, inserted, changed, deleted), s
def _set_diff(self, a, b):
removed = a.difference(b)
added = b.difference(a)
if not removed and not added:
return {}, 1.0
ranking = sorted(
(
(self._obj_diff(x, y)[1], x, y)
for x in removed
for y in added
),
reverse=True,
key=lambda x: x[0]
)
r2 = set(removed)
a2 = set(added)
n_common = len(a) - len(removed)
s_common = float(n_common)
for s, x, y in ranking:
if x in r2 and y in a2:
r2.discard(x)
a2.discard(y)
s_common += s
n_common += 1
if not r2 or not a2:
break
n_tot = len(a) + len(added)
s = s_common / n_tot if n_tot != 0 else 1.0
return self.options.syntax.emit_set_diff(a, b, s, added, removed), s
def _dict_diff(self, a, b):
removed = {}
nremoved = 0
nadded = 0
nmatched = 0
smatched = 0.0
added = {}
changed = {}
for k, v in a.items():
w = b.get(k, missing)
if w is missing:
nremoved += 1
removed[k] = v
else:
nmatched += 1
d, s = self._obj_diff(v, w)
if s < 1.0:
changed[k] = d
smatched += 0.5 + 0.5 * s
for k, v in b.items():
if k not in a:
nadded += 1
added[k] = v
n_tot = nremoved + nmatched + nadded
s = smatched / n_tot if n_tot != 0 else 1.0
return self.options.syntax.emit_dict_diff(a, b, s, added, changed, removed), s
def _obj_diff(self, a, b):
if a is b:
return self.options.syntax.emit_value_diff(a, b, 1.0), 1.0
if isinstance(a, dict) and isinstance(b, dict):
return self._dict_diff(a, b)
elif isinstance(a, tuple) and isinstance(b, tuple):
return self._list_diff(a, b)
elif isinstance(a, list) and isinstance(b, list):
return self._list_diff(a, b)
elif isinstance(a, set) and isinstance(b, set):
return self._set_diff(a, b)
elif a != b:
return self.options.syntax.emit_value_diff(a, b, 0.0), 0.0
else:
return self.options.syntax.emit_value_diff(a, b, 1.0), 1.0
def diff(self, a, b, fp=None):
if self.options.load:
a = self.options.loader(a)
b = self.options.loader(b)
d, s = self._obj_diff(a, b)
if self.options.marshal or self.options.dump:
d = self.marshal(d)
if self.options.dump:
return self.options.dumper(d, fp)
else:
return d
def similarity(self, a, b):
if self.options.load:
a = self.options.loader(a)
b = self.options.loader(b)
d, s = self._obj_diff(a, b)
return s
def patch(self, a, d, fp=None):
if self.options.load:
a = self.options.loader(a)
d = self.options.loader(d)
if self.options.marshal or self.options.load:
d = self.unmarshal(d)
b = self.options.syntax.patch(a, d)
if self.options.dump:
return self.options.dumper(b, fp)
else:
return b
def unpatch(self, b, d, fp=None):
if self.options.load:
b = self.options.loader(b)
d = self.options.loader(d)
if self.options.marshal or self.options.load:
d = self.unmarshal(d)
a = self.options.syntax.unpatch(b, d)
if self.options.dump:
return self.options.dumper(a, fp)
else:
return a
def _unescape(self, x):
if isinstance(x, string_types):
sym = self._symbol_map.get(x, None)
if sym is not None:
return sym
if x.startswith(self.options.escape_str):
return x[1:]
return x
def unmarshal(self, d):
if isinstance(d, dict):
return {
self._unescape(k): self.unmarshal(v)
for k, v in d.items()
}
elif isinstance(d, (list, tuple)):
return type(d)(
self.unmarshal(x)
for x in d
)
else:
return self._unescape(d)
def _escape(self, o):
if type(o) is Symbol:
return self.options.escape_str + o.label
if isinstance(o, string_types) and o.startswith(self.options.escape_str):
return self.options.escape_str + o
return o
def marshal(self, d):
if isinstance(d, dict):
return {
self._escape(k): self.marshal(v)
for k, v in d.items()
}
elif isinstance(d, (list, tuple)):
return type(d)(
self.marshal(x)
for x in d
)
else:
return self._escape(d)
| (syntax='compact', load=False, dump=False, marshal=False, loader=<jsondiff.JsonLoader object at 0x7f5da7f7c0a0>, dumper=<jsondiff.JsonDumper object at 0x7f5da7f7c040>, escape_str='$') | [
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18,145 | jsondiff | __init__ | null | def __init__(self, syntax='compact', load=False, dump=False, marshal=False,
loader=default_loader, dumper=default_dumper, escape_str='$'):
self.options = JsonDiffer.Options()
self.options.syntax = builtin_syntaxes.get(syntax, syntax)
self.options.load = load
self.options.dump = dump
self.options.marshal = marshal
self.options.loader = loader
self.options.dumper = dumper
self.options.escape_str = escape_str
self._symbol_map = {
escape_str + symbol.label: symbol
for symbol in _all_symbols_
}
| (self, syntax='compact', load=False, dump=False, marshal=False, loader=<jsondiff.JsonLoader object at 0x7f5da7f7c0a0>, dumper=<jsondiff.JsonDumper object at 0x7f5da7f7c040>, escape_str='$') | [
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18,146 | jsondiff | _dict_diff | null | def _dict_diff(self, a, b):
removed = {}
nremoved = 0
nadded = 0
nmatched = 0
smatched = 0.0
added = {}
changed = {}
for k, v in a.items():
w = b.get(k, missing)
if w is missing:
nremoved += 1
removed[k] = v
else:
nmatched += 1
d, s = self._obj_diff(v, w)
if s < 1.0:
changed[k] = d
smatched += 0.5 + 0.5 * s
for k, v in b.items():
if k not in a:
nadded += 1
added[k] = v
n_tot = nremoved + nmatched + nadded
s = smatched / n_tot if n_tot != 0 else 1.0
return self.options.syntax.emit_dict_diff(a, b, s, added, changed, removed), s
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18,147 | jsondiff | _escape | null | def _escape(self, o):
if type(o) is Symbol:
return self.options.escape_str + o.label
if isinstance(o, string_types) and o.startswith(self.options.escape_str):
return self.options.escape_str + o
return o
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18,148 | jsondiff | _list_diff | null | def _list_diff(self, X, Y):
# LCS
m = len(X)
n = len(Y)
# An (m+1) times (n+1) matrix
C = [[0 for j in range(n+1)] for i in range(m+1)]
for i in range(1, m+1):
for j in range(1, n+1):
_, s = self._obj_diff(X[i-1], Y[j-1])
# Following lines are part of the original LCS algorithm
# left in the code in case modification turns out to be problematic
#if X[i-1] == Y[j-1]:
# C[i][j] = C[i-1][j-1] + 1
#else:
C[i][j] = max(C[i][j-1], C[i-1][j], C[i-1][j-1] + s)
inserted = []
deleted = []
changed = {}
tot_s = 0.0
for sign, value, pos, s in self._list_diff_0(C, X, Y):
if sign == 1:
inserted.append((pos, value))
elif sign == -1:
deleted.insert(0, (pos, value))
elif sign == 0 and s < 1:
changed[pos] = value
tot_s += s
tot_n = len(X) + len(inserted)
if tot_n == 0:
s = 1.0
else:
s = tot_s / tot_n
return self.options.syntax.emit_list_diff(X, Y, s, inserted, changed, deleted), s
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18,149 | jsondiff | _list_diff_0 | null | def _list_diff_0(self, C, X, Y):
i, j = len(X), len(Y)
r = []
while True:
if i > 0 and j > 0:
d, s = self._obj_diff(X[i-1], Y[j-1])
if s > 0 and C[i][j] == C[i-1][j-1] + s:
r.append((0, d, j-1, s))
i, j = i - 1, j - 1
continue
if j > 0 and (i == 0 or C[i][j-1] >= C[i-1][j]):
r.append((1, Y[j-1], j-1, 0.0))
j = j - 1
continue
if i > 0 and (j == 0 or C[i][j-1] < C[i-1][j]):
r.append((-1, X[i-1], i-1, 0.0))
i = i - 1
continue
return reversed(r)
| (self, C, X, Y) | [
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18,150 | jsondiff | _obj_diff | null | def _obj_diff(self, a, b):
if a is b:
return self.options.syntax.emit_value_diff(a, b, 1.0), 1.0
if isinstance(a, dict) and isinstance(b, dict):
return self._dict_diff(a, b)
elif isinstance(a, tuple) and isinstance(b, tuple):
return self._list_diff(a, b)
elif isinstance(a, list) and isinstance(b, list):
return self._list_diff(a, b)
elif isinstance(a, set) and isinstance(b, set):
return self._set_diff(a, b)
elif a != b:
return self.options.syntax.emit_value_diff(a, b, 0.0), 0.0
else:
return self.options.syntax.emit_value_diff(a, b, 1.0), 1.0
| (self, a, b) | [
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18,151 | jsondiff | _set_diff | null | def _set_diff(self, a, b):
removed = a.difference(b)
added = b.difference(a)
if not removed and not added:
return {}, 1.0
ranking = sorted(
(
(self._obj_diff(x, y)[1], x, y)
for x in removed
for y in added
),
reverse=True,
key=lambda x: x[0]
)
r2 = set(removed)
a2 = set(added)
n_common = len(a) - len(removed)
s_common = float(n_common)
for s, x, y in ranking:
if x in r2 and y in a2:
r2.discard(x)
a2.discard(y)
s_common += s
n_common += 1
if not r2 or not a2:
break
n_tot = len(a) + len(added)
s = s_common / n_tot if n_tot != 0 else 1.0
return self.options.syntax.emit_set_diff(a, b, s, added, removed), s
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18,152 | jsondiff | _unescape | null | def _unescape(self, x):
if isinstance(x, string_types):
sym = self._symbol_map.get(x, None)
if sym is not None:
return sym
if x.startswith(self.options.escape_str):
return x[1:]
return x
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18,153 | jsondiff | diff | null | def diff(self, a, b, fp=None):
if self.options.load:
a = self.options.loader(a)
b = self.options.loader(b)
d, s = self._obj_diff(a, b)
if self.options.marshal or self.options.dump:
d = self.marshal(d)
if self.options.dump:
return self.options.dumper(d, fp)
else:
return d
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18,154 | jsondiff | marshal | null | def marshal(self, d):
if isinstance(d, dict):
return {
self._escape(k): self.marshal(v)
for k, v in d.items()
}
elif isinstance(d, (list, tuple)):
return type(d)(
self.marshal(x)
for x in d
)
else:
return self._escape(d)
| (self, d) | [
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18,155 | jsondiff | patch | null | def patch(self, a, d, fp=None):
if self.options.load:
a = self.options.loader(a)
d = self.options.loader(d)
if self.options.marshal or self.options.load:
d = self.unmarshal(d)
b = self.options.syntax.patch(a, d)
if self.options.dump:
return self.options.dumper(b, fp)
else:
return b
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18,156 | jsondiff | similarity | null | def similarity(self, a, b):
if self.options.load:
a = self.options.loader(a)
b = self.options.loader(b)
d, s = self._obj_diff(a, b)
return s
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18,157 | jsondiff | unmarshal | null | def unmarshal(self, d):
if isinstance(d, dict):
return {
self._unescape(k): self.unmarshal(v)
for k, v in d.items()
}
elif isinstance(d, (list, tuple)):
return type(d)(
self.unmarshal(x)
for x in d
)
else:
return self._unescape(d)
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18,158 | jsondiff | unpatch | null | def unpatch(self, b, d, fp=None):
if self.options.load:
b = self.options.loader(b)
d = self.options.loader(d)
if self.options.marshal or self.options.load:
d = self.unmarshal(d)
a = self.options.syntax.unpatch(b, d)
if self.options.dump:
return self.options.dumper(a, fp)
else:
return a
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18,159 | jsondiff | JsonDumper | null | class JsonDumper(object):
def __init__(self, **kwargs):
self.kwargs = kwargs
def __call__(self, obj, dest=None):
if dest is None:
return json.dumps(obj, **self.kwargs)
else:
return json.dump(obj, dest, **self.kwargs)
| (**kwargs) | [
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18,160 | jsondiff | __call__ | null | def __call__(self, obj, dest=None):
if dest is None:
return json.dumps(obj, **self.kwargs)
else:
return json.dump(obj, dest, **self.kwargs)
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18,161 | jsondiff | __init__ | null | def __init__(self, **kwargs):
self.kwargs = kwargs
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18,162 | jsondiff | JsonLoader | null | class JsonLoader(object):
def __init__(self, **kwargs):
self.kwargs = kwargs
def __call__(self, src):
if isinstance(src, string_types):
return json.loads(src, **self.kwargs)
else:
return json.load(src, **self.kwargs)
| (**kwargs) | [
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18,163 | jsondiff | __call__ | null | def __call__(self, src):
if isinstance(src, string_types):
return json.loads(src, **self.kwargs)
else:
return json.load(src, **self.kwargs)
| (self, src) | [
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18,165 | jsondiff.symbols | Symbol | null | class Symbol(object):
def __init__(self, label):
self.label = label
def __repr__(self):
return self.label
def __str__(self):
return "$" + self.label
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18,166 | jsondiff.symbols | __init__ | null | def __init__(self, label):
self.label = label
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18,167 | jsondiff.symbols | __repr__ | null | def __repr__(self):
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18,168 | jsondiff.symbols | __str__ | null | def __str__(self):
return "$" + self.label
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18,169 | jsondiff | SymmetricJsonDiffSyntax | null | class SymmetricJsonDiffSyntax(object):
def emit_set_diff(self, a, b, s, added, removed):
if s == 0.0 or len(removed) == len(a):
return [a, b]
else:
d = {}
if added:
d[add] = added
if removed:
d[discard] = removed
return d
def emit_list_diff(self, a, b, s, inserted, changed, deleted):
if s == 0.0:
return [a, b]
elif s == 1.0:
return {}
else:
d = changed
if inserted:
d[insert] = inserted
if deleted:
d[delete] = deleted
return d
def emit_dict_diff(self, a, b, s, added, changed, removed):
if s == 0.0:
return [a, b]
elif s == 1.0:
return {}
else:
d = changed
if added:
d[insert] = added
if removed:
d[delete] = removed
return d
def emit_value_diff(self, a, b, s):
if s == 1.0:
return {}
else:
return [a, b]
def patch(self, a, d):
if isinstance(d, list):
_, b = d
return b
elif isinstance(d, dict):
if not d:
return a
if isinstance(a, dict):
a = dict(a)
for k, v in d.items():
if k is delete:
for kdel, _ in v.items():
del a[kdel]
elif k is insert:
for kk, vv in v.items():
a[kk] = vv
else:
a[k] = self.patch(a[k], v)
return a
elif isinstance(a, (list, tuple)):
original_type = type(a)
a = list(a)
if delete in d:
for pos, value in d[delete]:
a.pop(pos)
if insert in d:
for pos, value in d[insert]:
a.insert(pos, value)
for k, v in d.items():
if k is not delete and k is not insert:
k = int(k)
a[k] = self.patch(a[k], v)
if original_type is not list:
a = original_type(a)
return a
elif isinstance(a, set):
a = set(a)
if discard in d:
for x in d[discard]:
a.discard(x)
if add in d:
for x in d[add]:
a.add(x)
return a
raise Exception("Invalid symmetric diff")
def unpatch(self, b, d):
if isinstance(d, list):
a, _ = d
return a
elif isinstance(d, dict):
if not d:
return b
if isinstance(b, dict):
b = dict(b)
for k, v in d.items():
if k is delete:
for kk, vv in v.items():
b[kk] = vv
elif k is insert:
for kk, vv in v.items():
del b[kk]
else:
b[k] = self.unpatch(b[k], v)
return b
elif isinstance(b, (list, tuple)):
original_type = type(b)
b = list(b)
for k, v in d.items():
if k is not delete and k is not insert:
k = int(k)
b[k] = self.unpatch(b[k], v)
if insert in d:
for pos, value in reversed(d[insert]):
b.pop(pos)
if delete in d:
for pos, value in reversed(d[delete]):
b.insert(pos, value)
if original_type is not list:
b = original_type(b)
return b
elif isinstance(b, set):
b = set(b)
if discard in d:
for x in d[discard]:
b.add(x)
if add in d:
for x in d[add]:
b.discard(x)
return b
raise Exception("Invalid symmetric diff")
| () | [
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18,170 | jsondiff | emit_dict_diff | null | def emit_dict_diff(self, a, b, s, added, changed, removed):
if s == 0.0:
return [a, b]
elif s == 1.0:
return {}
else:
d = changed
if added:
d[insert] = added
if removed:
d[delete] = removed
return d
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18,171 | jsondiff | emit_list_diff | null | def emit_list_diff(self, a, b, s, inserted, changed, deleted):
if s == 0.0:
return [a, b]
elif s == 1.0:
return {}
else:
d = changed
if inserted:
d[insert] = inserted
if deleted:
d[delete] = deleted
return d
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18,172 | jsondiff | emit_set_diff | null | def emit_set_diff(self, a, b, s, added, removed):
if s == 0.0 or len(removed) == len(a):
return [a, b]
else:
d = {}
if added:
d[add] = added
if removed:
d[discard] = removed
return d
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18,173 | jsondiff | emit_value_diff | null | def emit_value_diff(self, a, b, s):
if s == 1.0:
return {}
else:
return [a, b]
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18,174 | jsondiff | patch | null | def patch(self, a, d):
if isinstance(d, list):
_, b = d
return b
elif isinstance(d, dict):
if not d:
return a
if isinstance(a, dict):
a = dict(a)
for k, v in d.items():
if k is delete:
for kdel, _ in v.items():
del a[kdel]
elif k is insert:
for kk, vv in v.items():
a[kk] = vv
else:
a[k] = self.patch(a[k], v)
return a
elif isinstance(a, (list, tuple)):
original_type = type(a)
a = list(a)
if delete in d:
for pos, value in d[delete]:
a.pop(pos)
if insert in d:
for pos, value in d[insert]:
a.insert(pos, value)
for k, v in d.items():
if k is not delete and k is not insert:
k = int(k)
a[k] = self.patch(a[k], v)
if original_type is not list:
a = original_type(a)
return a
elif isinstance(a, set):
a = set(a)
if discard in d:
for x in d[discard]:
a.discard(x)
if add in d:
for x in d[add]:
a.add(x)
return a
raise Exception("Invalid symmetric diff")
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18,175 | jsondiff | unpatch | null | def unpatch(self, b, d):
if isinstance(d, list):
a, _ = d
return a
elif isinstance(d, dict):
if not d:
return b
if isinstance(b, dict):
b = dict(b)
for k, v in d.items():
if k is delete:
for kk, vv in v.items():
b[kk] = vv
elif k is insert:
for kk, vv in v.items():
del b[kk]
else:
b[k] = self.unpatch(b[k], v)
return b
elif isinstance(b, (list, tuple)):
original_type = type(b)
b = list(b)
for k, v in d.items():
if k is not delete and k is not insert:
k = int(k)
b[k] = self.unpatch(b[k], v)
if insert in d:
for pos, value in reversed(d[insert]):
b.pop(pos)
if delete in d:
for pos, value in reversed(d[delete]):
b.insert(pos, value)
if original_type is not list:
b = original_type(b)
return b
elif isinstance(b, set):
b = set(b)
if discard in d:
for x in d[discard]:
b.add(x)
if add in d:
for x in d[add]:
b.discard(x)
return b
raise Exception("Invalid symmetric diff")
| (self, b, d) | [
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18,176 | jsondiff | diff | null | def diff(a, b, fp=None, cls=JsonDiffer, **kwargs):
return cls(**kwargs).diff(a, b, fp)
| (a, b, fp=None, cls=<class 'jsondiff.JsonDiffer'>, **kwargs) | [
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18,178 | jsondiff | patch | null | def patch(a, d, fp=None, cls=JsonDiffer, **kwargs):
return cls(**kwargs).patch(a, d, fp)
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18,179 | jsondiff | similarity | null | def similarity(a, b, cls=JsonDiffer, **kwargs):
return cls(**kwargs).similarity(a, b)
| (a, b, cls=<class 'jsondiff.JsonDiffer'>, **kwargs) | [
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18,183 | aiohttp.client_reqrep | ClientResponse | null | class ClientResponse(HeadersMixin):
# Some of these attributes are None when created,
# but will be set by the start() method.
# As the end user will likely never see the None values, we cheat the types below.
# from the Status-Line of the response
version: Optional[HttpVersion] = None # HTTP-Version
status: int = None # type: ignore[assignment] # Status-Code
reason: Optional[str] = None # Reason-Phrase
content: StreamReader = None # type: ignore[assignment] # Payload stream
_headers: CIMultiDictProxy[str] = None # type: ignore[assignment]
_raw_headers: RawHeaders = None # type: ignore[assignment]
_connection = None # current connection
_source_traceback: Optional[traceback.StackSummary] = None
# set up by ClientRequest after ClientResponse object creation
# post-init stage allows to not change ctor signature
_closed = True # to allow __del__ for non-initialized properly response
_released = False
__writer = None
def __init__(
self,
method: str,
url: URL,
*,
writer: "asyncio.Task[None]",
continue100: Optional["asyncio.Future[bool]"],
timer: BaseTimerContext,
request_info: RequestInfo,
traces: List["Trace"],
loop: asyncio.AbstractEventLoop,
session: "ClientSession",
) -> None:
assert isinstance(url, URL)
self.method = method
self.cookies = SimpleCookie()
self._real_url = url
self._url = url.with_fragment(None)
self._body: Any = None
self._writer: Optional[asyncio.Task[None]] = writer
self._continue = continue100 # None by default
self._closed = True
self._history: Tuple[ClientResponse, ...] = ()
self._request_info = request_info
self._timer = timer if timer is not None else TimerNoop()
self._cache: Dict[str, Any] = {}
self._traces = traces
self._loop = loop
# store a reference to session #1985
self._session: Optional[ClientSession] = session
# Save reference to _resolve_charset, so that get_encoding() will still
# work after the response has finished reading the body.
if session is None:
# TODO: Fix session=None in tests (see ClientRequest.__init__).
self._resolve_charset: Callable[
["ClientResponse", bytes], str
] = lambda *_: "utf-8"
else:
self._resolve_charset = session._resolve_charset
if loop.get_debug():
self._source_traceback = traceback.extract_stack(sys._getframe(1))
def __reset_writer(self, _: object = None) -> None:
self.__writer = None
@property
def _writer(self) -> Optional["asyncio.Task[None]"]:
return self.__writer
@_writer.setter
def _writer(self, writer: Optional["asyncio.Task[None]"]) -> None:
if self.__writer is not None:
self.__writer.remove_done_callback(self.__reset_writer)
self.__writer = writer
if writer is not None:
writer.add_done_callback(self.__reset_writer)
@reify
def url(self) -> URL:
return self._url
@reify
def url_obj(self) -> URL:
warnings.warn("Deprecated, use .url #1654", DeprecationWarning, stacklevel=2)
return self._url
@reify
def real_url(self) -> URL:
return self._real_url
@reify
def host(self) -> str:
assert self._url.host is not None
return self._url.host
@reify
def headers(self) -> "CIMultiDictProxy[str]":
return self._headers
@reify
def raw_headers(self) -> RawHeaders:
return self._raw_headers
@reify
def request_info(self) -> RequestInfo:
return self._request_info
@reify
def content_disposition(self) -> Optional[ContentDisposition]:
raw = self._headers.get(hdrs.CONTENT_DISPOSITION)
if raw is None:
return None
disposition_type, params_dct = multipart.parse_content_disposition(raw)
params = MappingProxyType(params_dct)
filename = multipart.content_disposition_filename(params)
return ContentDisposition(disposition_type, params, filename)
def __del__(self, _warnings: Any = warnings) -> None:
if self._closed:
return
if self._connection is not None:
self._connection.release()
self._cleanup_writer()
if self._loop.get_debug():
kwargs = {"source": self}
_warnings.warn(f"Unclosed response {self!r}", ResourceWarning, **kwargs)
context = {"client_response": self, "message": "Unclosed response"}
if self._source_traceback:
context["source_traceback"] = self._source_traceback
self._loop.call_exception_handler(context)
def __repr__(self) -> str:
out = io.StringIO()
ascii_encodable_url = str(self.url)
if self.reason:
ascii_encodable_reason = self.reason.encode(
"ascii", "backslashreplace"
).decode("ascii")
else:
ascii_encodable_reason = "None"
print(
"<ClientResponse({}) [{} {}]>".format(
ascii_encodable_url, self.status, ascii_encodable_reason
),
file=out,
)
print(self.headers, file=out)
return out.getvalue()
@property
def connection(self) -> Optional["Connection"]:
return self._connection
@reify
def history(self) -> Tuple["ClientResponse", ...]:
"""A sequence of of responses, if redirects occurred."""
return self._history
@reify
def links(self) -> "MultiDictProxy[MultiDictProxy[Union[str, URL]]]":
links_str = ", ".join(self.headers.getall("link", []))
if not links_str:
return MultiDictProxy(MultiDict())
links: MultiDict[MultiDictProxy[Union[str, URL]]] = MultiDict()
for val in re.split(r",(?=\s*<)", links_str):
match = re.match(r"\s*<(.*)>(.*)", val)
if match is None: # pragma: no cover
# the check exists to suppress mypy error
continue
url, params_str = match.groups()
params = params_str.split(";")[1:]
link: MultiDict[Union[str, URL]] = MultiDict()
for param in params:
match = re.match(r"^\s*(\S*)\s*=\s*(['\"]?)(.*?)(\2)\s*$", param, re.M)
if match is None: # pragma: no cover
# the check exists to suppress mypy error
continue
key, _, value, _ = match.groups()
link.add(key, value)
key = link.get("rel", url)
link.add("url", self.url.join(URL(url)))
links.add(str(key), MultiDictProxy(link))
return MultiDictProxy(links)
async def start(self, connection: "Connection") -> "ClientResponse":
"""Start response processing."""
self._closed = False
self._protocol = connection.protocol
self._connection = connection
with self._timer:
while True:
# read response
try:
protocol = self._protocol
message, payload = await protocol.read() # type: ignore[union-attr]
except http.HttpProcessingError as exc:
raise ClientResponseError(
self.request_info,
self.history,
status=exc.code,
message=exc.message,
headers=exc.headers,
) from exc
if message.code < 100 or message.code > 199 or message.code == 101:
break
if self._continue is not None:
set_result(self._continue, True)
self._continue = None
# payload eof handler
payload.on_eof(self._response_eof)
# response status
self.version = message.version
self | (method: str, url: yarl.URL, *, writer: 'asyncio.Task[None]', continue100: Optional[ForwardRef('asyncio.Future[bool]')], timer: aiohttp.helpers.BaseTimerContext, request_info: aiohttp.client_reqrep.RequestInfo, traces: List[ForwardRef('Trace')], loop: asyncio.events.AbstractEventLoop, session: 'ClientSession') -> None | [
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-0.008918521925807,
-0.015398507937788963,
-0.02082214690744877,
0.01980258710682392,
0.007404948119074106,
0.01821543648838997,
-0.0401727631688118,
0.05966002121567726,
0.04298969358205795,
0.006059549283236265,
-0.03645189478993416,
0.05289098620414734,
-0.01286537479609251,
0.05183989182114601
] |
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