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16,679 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | BayesianRuleListClassifier |
This is a scikit-learn compatible wrapper for the Bayesian Rule List
classifier developed by Benjamin Letham. It produces a highly
interpretable model (a list of decision rules) by sampling many different
rule lists, trying to optimize for compactness and predictive performance.
Parameters
----------
listlengthprior : int, optional (default=3)
Prior hyperparameter for expected list length (excluding null rule)
listwidthprior : int, optional (default=1)
Prior hyperparameter for expected list width (excluding null rule)
maxcardinality : int, optional (default=2)
Maximum cardinality of an itemset
minsupport : float, optional (default=0.1)
Minimum support (fraction between 0 and 1) of an itemset
alpha : array_like, shape = [n_classes]
prior hyperparameter for multinomial pseudocounts
n_chains : int, optional (default=3)
Number of MCMC chains for inference
max_iter : int, optional (default=50000)
Maximum number of iterations
class1label: str, optional (default="class 1")
Label or description of what the positive class (with y=1) means
verbose: bool, optional (default=True)
Verbose output
random_state: int
Random seed
| class BayesianRuleListClassifier(BaseEstimator, RuleList, ClassifierMixin):
"""
This is a scikit-learn compatible wrapper for the Bayesian Rule List
classifier developed by Benjamin Letham. It produces a highly
interpretable model (a list of decision rules) by sampling many different
rule lists, trying to optimize for compactness and predictive performance.
Parameters
----------
listlengthprior : int, optional (default=3)
Prior hyperparameter for expected list length (excluding null rule)
listwidthprior : int, optional (default=1)
Prior hyperparameter for expected list width (excluding null rule)
maxcardinality : int, optional (default=2)
Maximum cardinality of an itemset
minsupport : float, optional (default=0.1)
Minimum support (fraction between 0 and 1) of an itemset
alpha : array_like, shape = [n_classes]
prior hyperparameter for multinomial pseudocounts
n_chains : int, optional (default=3)
Number of MCMC chains for inference
max_iter : int, optional (default=50000)
Maximum number of iterations
class1label: str, optional (default="class 1")
Label or description of what the positive class (with y=1) means
verbose: bool, optional (default=True)
Verbose output
random_state: int
Random seed
"""
def __init__(self,
listlengthprior=3,
listwidthprior=1,
maxcardinality=2,
minsupport=0.1,
alpha=np.array([1., 1.]),
n_chains=3,
max_iter=50000,
class1label="class 1",
verbose=False,
random_state=42):
self.listlengthprior = listlengthprior
self.listwidthprior = listwidthprior
self.maxcardinality = maxcardinality
self.minsupport = minsupport
self.alpha = alpha
self.n_chains = n_chains
self.max_iter = max_iter
self.class1label = class1label
self.verbose = verbose
self._zmin = 1
self.thinning = 1 # The thinning rate
self.burnin = self.max_iter // 2 # the number of samples to drop as burn-in in-simulation
self.d_star = None
self.random_state = random_state
self.seed()
def seed(self):
if self.random_state is not None:
random.seed(self.random_state)
np.random.seed(self.random_state)
def _setlabels(self, X, feature_names=[]):
if len(feature_names) == 0:
if type(X) == pd.DataFrame and ('object' in str(X.columns.dtype) or 'str' in str(X.columns.dtype)):
feature_names = X.columns
else:
feature_names = ["ft" + str(i + 1) for i in range(len(X[0]))]
self.feature_names = feature_names
def fit(self, X, y, feature_names: list = None, verbose=False):
"""Fit rule lists to data.
Note: The BRL algorithm requires numeric features to be discretized into bins
prior to fitting. See imodels.discretization or sklearn.preprocessing for
helpful utilities.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array_like, shape = [n_samples]
Labels
feature_names : array_like, shape = [n_features], optional (default: [])
String labels for each feature.
If empty and X is a DataFrame, column labels are used.
If empty and X is not a DataFrame, then features are simply enumerated
verbose : bool
Currently doesn't do anything
Returns
-------
self : returns an instance of self.
"""
self.seed()
if len(set(y)) != 2:
raise ValueError("Only binary classification is supported at this time!")
X, y = check_X_y(X, y)
check_classification_targets(y)
self.n_features_in_ = X.shape[1]
self.classes_ = unique_labels(y)
# Check that all features are either categorical or discretized
if not np.all((X == 1) | (X == 0)):
raise ValueError("All numeric features must be discretized prior to fitting!")
self.feature_dict_ = get_feature_dict(X.shape[1], feature_names)
self.feature_placeholders = np.array(list(self.feature_dict_.keys()))
self.feature_names = np.array(list(self.feature_dict_.values()))
X_df = pd.DataFrame(X, columns=self.feature_placeholders)
itemsets = extract_fpgrowth(X_df, minsupport=self.minsupport,
maxcardinality=self.maxcardinality,
verbose=verbose)
# Now form the data-vs.-lhs set
# X[j] is the set of data points that contain itemset j (that is, satisfy rule j)
for col in X_df.columns:
# X_df[c] = [c if x == 1 else '' for x in list(X_df[c])]
X_df[col] = X_df[col].replace({1: col, 0: ''})
itemset_support_inds = [{}] * (len(itemsets) + 1)
itemset_support_inds[0] = set(range(X_df.shape[0])) # the default rule satisfies all data
for (j, lhs) in enumerate(itemsets):
itemset_support_inds[j + 1] = set(
[i for (i, xi) in enumerate(X_df.values) if set(lhs).issubset(xi)])
# now form lhs_len
lhs_len = [0]
for lhs in itemsets:
lhs_len.append(len(lhs))
nruleslen = Counter(lhs_len)
lhs_len = np.array(lhs_len)
itemsets_all = ['null']
itemsets_all.extend(itemsets)
self.itemsets = itemsets_all
Xtrain = itemset_support_inds
Ytrain = np.vstack((1 - np.array(y), y)).T.astype(int)
permsdic = defaultdict(default_permsdic) # We will store here the MCMC results
# Do MCMC
res, Rhat = run_bdl_multichain_serial(
self.max_iter, self.thinning, self.alpha, self.listlengthprior,
self.listwidthprior, Xtrain, Ytrain, nruleslen, lhs_len,
self.maxcardinality, permsdic, self.burnin, self.n_chains,
[None] * self.n_chains, verbose=self.verbose, seed=self.random_state)
# Merge the chains
permsdic = merge_chains(res)
# The point estimate, BRL-point
self.d_star = get_point_estimate(permsdic, lhs_len, Xtrain, Ytrain, self.alpha, nruleslen, self.maxcardinality,
self.listlengthprior, self.listwidthprior,
verbose=self.verbose) # get the point estimate
if self.d_star:
# Compute the rule consequent
self.theta, self.ci_theta = get_rule_rhs(Xtrain, Ytrain, self.d_star, self.alpha, True)
self.final_itemsets = np.array(self.itemsets, dtype=object)[self.d_star]
rule_strs = itemsets_to_rules(self.final_itemsets)
self.rules_without_feature_names_ = [Rule(r) for r in rule_strs]
self.rules_ = [
replace_feature_name(rule, self.feature_dict_) for rule in self.rules_without_feature_names_
]
self.complexity_ = self._get_complexity()
return self
def _get_complexity(self):
n_rule_terms = sum([len(iset) for iset in self.final_itemsets if type(iset) != str])
return n_rule_terms + 1
# def __repr__(self, decimals=1):
# if self.d_star:
# detect = ""
# if self.class1label != "class 1":
# detect = "for detecting " + self.class1label
# header = "Trained RuleListClassifier " + detect + "\n"
# separator = "".join(["="] * len(header)) + "\n"
# s = ""
# for i, j in enumerate(self.d_star):
# if self.itemsets[j] != 'null':
# condition = "ELSE IF " + (
# " AND ".join([str(self.itemsets[j][k]) for k in range(len(self.itemsets[j]))])) + " THEN"
# else:
# condition = "ELSE"
# s += cond | (listlengthprior=3, listwidthprior=1, maxcardinality=2, minsupport=0.1, alpha=array([1., 1.]), n_chains=3, max_iter=50000, class1label='class 1', verbose=False, random_state=42) | [
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16,681 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | __init__ | null | def __init__(self,
listlengthprior=3,
listwidthprior=1,
maxcardinality=2,
minsupport=0.1,
alpha=np.array([1., 1.]),
n_chains=3,
max_iter=50000,
class1label="class 1",
verbose=False,
random_state=42):
self.listlengthprior = listlengthprior
self.listwidthprior = listwidthprior
self.maxcardinality = maxcardinality
self.minsupport = minsupport
self.alpha = alpha
self.n_chains = n_chains
self.max_iter = max_iter
self.class1label = class1label
self.verbose = verbose
self._zmin = 1
self.thinning = 1 # The thinning rate
self.burnin = self.max_iter // 2 # the number of samples to drop as burn-in in-simulation
self.d_star = None
self.random_state = random_state
self.seed()
| (self, listlengthprior=3, listwidthprior=1, maxcardinality=2, minsupport=0.1, alpha=array([1., 1.]), n_chains=3, max_iter=50000, class1label='class 1', verbose=False, random_state=42) | [
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16,682 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | __repr__ | null | def __repr__(self, decimals=1):
if self.d_star:
detect = ""
if self.class1label != "class 1":
detect = "for detecting " + self.class1label
header = "Trained RuleListClassifier " + detect + "\n"
separator = "".join(["="] * len(header)) + "\n"
s = ""
for i in range(len(self.rules_) + 1):
if i != len(self.rules_):
condition = "ELSE IF " + str(self.rules_[i]) + " THEN"
else:
condition = "ELSE"
s += condition + " probability of " + self.class1label + ": " + str(
np.round(self.theta[i] * 100, decimals)) + "% (" + str(
np.round(self.ci_theta[i][0] * 100, decimals)) + "%-" + str(
np.round(self.ci_theta[i][1] * 100, decimals)) + "%)\n"
return header + separator + s[5:] + separator[1:]
else:
return "(Untrained RuleListClassifier)"
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16,687 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | _get_complexity | null | def _get_complexity(self):
n_rule_terms = sum([len(iset) for iset in self.final_itemsets if type(iset) != str])
return n_rule_terms + 1
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16,694 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | _setlabels | null | def _setlabels(self, X, feature_names=[]):
if len(feature_names) == 0:
if type(X) == pd.DataFrame and ('object' in str(X.columns.dtype) or 'str' in str(X.columns.dtype)):
feature_names = X.columns
else:
feature_names = ["ft" + str(i + 1) for i in range(len(X[0]))]
self.feature_names = feature_names
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16,695 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | _to_itemset_indices | null | def _to_itemset_indices(self, X_df_onehot):
# X[j] is the set of data points that contain itemset j (that is, satisfy rule j)
for c in X_df_onehot.columns:
X_df_onehot[c] = [c if x == 1 else '' for x in list(X_df_onehot[c])]
X = [set() for j in range(len(self.itemsets))]
X[0] = set(range(X_df_onehot.shape[0])) # the default rule satisfies all data
for (j, lhs) in enumerate(self.itemsets):
if j > 0:
X[j] = set([i for (i, xi) in enumerate(X_df_onehot.values) if set(lhs).issubset(xi)])
return X
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] |
16,698 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | fit | Fit rule lists to data.
Note: The BRL algorithm requires numeric features to be discretized into bins
prior to fitting. See imodels.discretization or sklearn.preprocessing for
helpful utilities.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array_like, shape = [n_samples]
Labels
feature_names : array_like, shape = [n_features], optional (default: [])
String labels for each feature.
If empty and X is a DataFrame, column labels are used.
If empty and X is not a DataFrame, then features are simply enumerated
verbose : bool
Currently doesn't do anything
Returns
-------
self : returns an instance of self.
| def fit(self, X, y, feature_names: list = None, verbose=False):
"""Fit rule lists to data.
Note: The BRL algorithm requires numeric features to be discretized into bins
prior to fitting. See imodels.discretization or sklearn.preprocessing for
helpful utilities.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array_like, shape = [n_samples]
Labels
feature_names : array_like, shape = [n_features], optional (default: [])
String labels for each feature.
If empty and X is a DataFrame, column labels are used.
If empty and X is not a DataFrame, then features are simply enumerated
verbose : bool
Currently doesn't do anything
Returns
-------
self : returns an instance of self.
"""
self.seed()
if len(set(y)) != 2:
raise ValueError("Only binary classification is supported at this time!")
X, y = check_X_y(X, y)
check_classification_targets(y)
self.n_features_in_ = X.shape[1]
self.classes_ = unique_labels(y)
# Check that all features are either categorical or discretized
if not np.all((X == 1) | (X == 0)):
raise ValueError("All numeric features must be discretized prior to fitting!")
self.feature_dict_ = get_feature_dict(X.shape[1], feature_names)
self.feature_placeholders = np.array(list(self.feature_dict_.keys()))
self.feature_names = np.array(list(self.feature_dict_.values()))
X_df = pd.DataFrame(X, columns=self.feature_placeholders)
itemsets = extract_fpgrowth(X_df, minsupport=self.minsupport,
maxcardinality=self.maxcardinality,
verbose=verbose)
# Now form the data-vs.-lhs set
# X[j] is the set of data points that contain itemset j (that is, satisfy rule j)
for col in X_df.columns:
# X_df[c] = [c if x == 1 else '' for x in list(X_df[c])]
X_df[col] = X_df[col].replace({1: col, 0: ''})
itemset_support_inds = [{}] * (len(itemsets) + 1)
itemset_support_inds[0] = set(range(X_df.shape[0])) # the default rule satisfies all data
for (j, lhs) in enumerate(itemsets):
itemset_support_inds[j + 1] = set(
[i for (i, xi) in enumerate(X_df.values) if set(lhs).issubset(xi)])
# now form lhs_len
lhs_len = [0]
for lhs in itemsets:
lhs_len.append(len(lhs))
nruleslen = Counter(lhs_len)
lhs_len = np.array(lhs_len)
itemsets_all = ['null']
itemsets_all.extend(itemsets)
self.itemsets = itemsets_all
Xtrain = itemset_support_inds
Ytrain = np.vstack((1 - np.array(y), y)).T.astype(int)
permsdic = defaultdict(default_permsdic) # We will store here the MCMC results
# Do MCMC
res, Rhat = run_bdl_multichain_serial(
self.max_iter, self.thinning, self.alpha, self.listlengthprior,
self.listwidthprior, Xtrain, Ytrain, nruleslen, lhs_len,
self.maxcardinality, permsdic, self.burnin, self.n_chains,
[None] * self.n_chains, verbose=self.verbose, seed=self.random_state)
# Merge the chains
permsdic = merge_chains(res)
# The point estimate, BRL-point
self.d_star = get_point_estimate(permsdic, lhs_len, Xtrain, Ytrain, self.alpha, nruleslen, self.maxcardinality,
self.listlengthprior, self.listwidthprior,
verbose=self.verbose) # get the point estimate
if self.d_star:
# Compute the rule consequent
self.theta, self.ci_theta = get_rule_rhs(Xtrain, Ytrain, self.d_star, self.alpha, True)
self.final_itemsets = np.array(self.itemsets, dtype=object)[self.d_star]
rule_strs = itemsets_to_rules(self.final_itemsets)
self.rules_without_feature_names_ = [Rule(r) for r in rule_strs]
self.rules_ = [
replace_feature_name(rule, self.feature_dict_) for rule in self.rules_without_feature_names_
]
self.complexity_ = self._get_complexity()
return self
| (self, X, y, feature_names: Optional[list] = None, verbose=False) | [
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16,701 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | predict | Perform classification on samples in X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
y_pred : array, shape = [n_samples]
Class labels for samples in X.
| def predict(self, X, threshold=0.1):
"""Perform classification on samples in X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
y_pred : array, shape = [n_samples]
Class labels for samples in X.
"""
check_is_fitted(self)
X = check_array(X)
# print('predicting!')
# print('preds_proba', self.predict_proba(X)[:, 1])
return 1 * (self.predict_proba(X)[:, 1] >= threshold)
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16,702 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | predict_proba | Compute probabilities of possible outcomes for samples in X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
T : array-like, shape = [n_samples, n_classes]
Returns the probability of the sample for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute `classes_`.
| def predict_proba(self, X):
"""Compute probabilities of possible outcomes for samples in X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
T : array-like, shape = [n_samples, n_classes]
Returns the probability of the sample for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute `classes_`.
"""
check_is_fitted(self)
X = check_array(X)
D = pd.DataFrame(X, columns=self.feature_placeholders)
N = len(D)
X2 = self._to_itemset_indices(D)
P = preds_d_t(X2, np.zeros((N, 1), dtype=int), self.d_star, self.theta)
return np.vstack((1 - P, P)).T
| (self, X) | [
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16,704 | imodels.rule_list.bayesian_rule_list.bayesian_rule_list | seed | null | def seed(self):
if self.random_state is not None:
random.seed(self.random_state)
np.random.seed(self.random_state)
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16,705 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` 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 ``fit`` 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 ``fit``.
- ``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
----------
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
verbose : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``verbose`` parameter in ``fit``.
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.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier, *, feature_names: Union[bool, NoneType, str] = '$UNCHANGED$', verbose: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier | [
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16,707 | 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
----------
threshold : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``threshold`` 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.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier, *, threshold: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier | [
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16,709 | imodels.rule_set.brs | BayesianRuleSetClassifier | Bayesian or-of-and algorithm.
Generates patterns that satisfy the minimum support and maximum length and then select the Nrules rules that have the highest entropy.
In function SA_patternbased, each local maximum is stored in maps and the best BOA is returned.
Remember here the BOA contains only the index of selected rules from Nrules self.rules_
| class BayesianRuleSetClassifier(RuleSet, BaseEstimator, ClassifierMixin):
'''Bayesian or-of-and algorithm.
Generates patterns that satisfy the minimum support and maximum length and then select the Nrules rules that have the highest entropy.
In function SA_patternbased, each local maximum is stored in maps and the best BOA is returned.
Remember here the BOA contains only the index of selected rules from Nrules self.rules_
'''
def __init__(self, n_rules: int = 2000,
supp=5, maxlen: int = 10,
num_iterations=5000, num_chains=3, q=0.1,
alpha_pos=100, beta_pos=1,
alpha_neg=100, beta_neg=1,
alpha_l=None, beta_l=None,
discretization_method='randomforest', random_state=0):
'''
Params
------
n_rules
number of rules to be used in SA_patternbased and also the output of generate_rules
supp
The higher this supp, the 'larger' a pattern is. 5% is a generally good number
maxlen
maximum length of a pattern
num_iterations
number of iterations in each chain
num_chains
number of chains in the simulated annealing search algorithm
q
alpha_pos
$\rho = alpha/(alpha+beta)$. Make sure $\rho$ is close to one when choosing alpha and beta
The alpha and beta parameters alter the prior distributions for different rules
beta_pos
alpha_neg
beta_neg
alpha_l
beta_l
discretization_method
discretization method
'''
self.n_rules = n_rules
self.supp = supp
self.maxlen = maxlen
self.num_iterations = num_iterations
self.num_chains = num_chains
self.q = q
self.alpha_pos = alpha_pos
self.beta_pos = beta_pos
self.alpha_neg = alpha_neg
self.beta_neg = beta_neg
self.discretization_method = discretization_method
self.alpha_l = alpha_l
self.beta_l = beta_l
self.random_state = 0
def fit(self, X, y, feature_names: list = None, init=[], verbose=False):
'''
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array_like, shape = [n_samples]
Labels
feature_names : array_like, shape = [n_features], optional (default: [])
String labels for each feature.
If empty and X is a DataFrame, column labels are used.
If empty and X is not a DataFrame, then features are simply enumerated
'''
# check inputs
self.attr_level_num = defaultdict(int) # any missing value defaults to 0
self.attr_names = []
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
np.random.seed(self.random_state)
# convert to pandas DataFrame
X = pd.DataFrame(X, columns=feature_names)
for i, name in enumerate(X.columns):
self.attr_level_num[name] += 1
self.attr_names.append(name)
self.attr_names_orig = deepcopy(self.attr_names)
self.attr_names = list(set(self.attr_names))
# set up patterns
self._set_pattern_space()
# parameter checking
if self.alpha_l is None or self.beta_l is None or len(self.alpha_l) != self.maxlen or len(
self.beta_l) != self.maxlen:
if verbose:
print('No or wrong input for alpha_l and beta_l - the model will use default parameters.')
self.C = [1.0 / self.maxlen] * self.maxlen
self.C.insert(0, -1)
self.alpha_l = [10] * (self.maxlen + 1)
self.beta_l = [10 * self.pattern_space[i] / self.C[i] for i in range(self.maxlen + 1)]
else:
self.alpha_l = [1] + list(self.alpha_l)
self.beta_l = [1] + list(self.beta_l)
# setup
self._generate_rules(X, y, verbose)
n_rules_current = len(self.rules_)
self.rules_len_list = [len(rule) for rule in self.rules_]
maps = defaultdict(list)
T0 = 1000 # initial temperature for simulated annealing
split = 0.7 * self.num_iterations
# run simulated annealing
for chain in range(self.num_chains):
# initialize with a random pattern set
if init != []:
rules_curr = init.copy()
else:
assert n_rules_current > 1, f'Only {n_rules_current} potential rules found, change hyperparams to allow for more'
N = sample(range(1, min(8, n_rules_current), 1), 1)[0]
rules_curr = sample(range(n_rules_current), N)
rules_curr_norm = self._normalize(rules_curr)
pt_curr = -100000000000
maps[chain].append(
[-1, [pt_curr / 3, pt_curr / 3, pt_curr / 3], rules_curr, [self.rules_[i] for i in rules_curr]])
for iter in range(self.num_iterations):
if iter >= split:
p = np.array(range(1 + len(maps[chain])))
p = np.array(list(_accumulate(p)))
p = p / p[-1]
index = _find_lt(p, random())
rules_curr = maps[chain][index][2].copy()
rules_curr_norm = maps[chain][index][2].copy()
# propose new rules
rules_new, rules_norm = self._propose(rules_curr.copy(), rules_curr_norm.copy(), self.q, y)
# compute probability of new rules
cfmatrix, prob = self._compute_prob(rules_new, y)
T = T0 ** (1 - iter / self.num_iterations) # temperature for simulated annealing
pt_new = sum(prob)
with warnings.catch_warnings():
if not verbose:
warnings.simplefilter("ignore")
alpha = np.exp(float(pt_new - pt_curr) / T)
if pt_new > sum(maps[chain][-1][1]):
maps[chain].append([iter, prob, rules_new, [self.rules_[i] for i in rules_new]])
if verbose:
print((
'\n** chain = {}, max at iter = {} ** \n accuracy = {}, TP = {},FP = {}, TN = {}, FN = {}'
'\n pt_new is {}, prior_ChsRules={}, likelihood_1 = {}, likelihood_2 = {}\n').format(
chain, iter, (cfmatrix[0] + cfmatrix[2] + 0.0) / len(y), cfmatrix[0], cfmatrix[1],
cfmatrix[2], cfmatrix[3], sum(prob), prob[0], prob[1], prob[2])
)
self._print_rules(rules_new)
print(rules_new)
if random() <= alpha:
rules_curr_norm, rules_curr, pt_curr = rules_norm.copy(), rules_new.copy(), pt_new
pt_max = [sum(maps[chain][-1][1]) for chain in range(self.num_chains)]
index = pt_max.index(max(pt_max))
self.rules_ = maps[index][-1][3]
return self
def __str__(self):
return ' '.join(str(r) for r in self.rules_)
def predict(self, X):
check_is_fitted(self)
if isinstance(X, np.ndarray):
df = pd.DataFrame(X, columns=self.attr_names_orig)
else:
df = X
Z = [[]] * len(self.rules_)
dfn = 1 - df # df has negative associations
dfn.columns = [name.strip() + '_neg' for name in df.columns]
df = pd.concat([df, dfn], axis=1)
for i, rule in enumerate(self.rules_):
Z[i] = (np.sum(df[list(rule)], axis=1) == len(rule)).astype(int)
Yhat = (np.sum(Z, axis=0) > 0).astype(int)
return Yhat
def predict_proba(self, X):
raise Exception('BOA does not support predicted probabilities.')
def _set_pattern_space(self):
"""Compute the rule space from the levels in each attribute
"""
# add feat_neg to each existing feature feat
for item in self.attr_names:
self.attr_level_num[item + '_neg'] = self.attr_level_num[item]
| (n_rules: int = 2000, supp=5, maxlen: int = 10, num_iterations=5000, num_chains=3, q=0.1, alpha_pos=100, beta_pos=1, alpha_neg=100, beta_neg=1, alpha_l=None, beta_l=None, discretization_method='randomforest', random_state=0) | [
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16,711 | imodels.rule_set.brs | __init__ |
Params
------
n_rules
number of rules to be used in SA_patternbased and also the output of generate_rules
supp
The higher this supp, the 'larger' a pattern is. 5% is a generally good number
maxlen
maximum length of a pattern
num_iterations
number of iterations in each chain
num_chains
number of chains in the simulated annealing search algorithm
q
alpha_pos
$
ho = alpha/(alpha+beta)$. Make sure $
ho$ is close to one when choosing alpha and beta
The alpha and beta parameters alter the prior distributions for different rules
beta_pos
alpha_neg
beta_neg
alpha_l
beta_l
discretization_method
discretization method
| def __init__(self, n_rules: int = 2000,
supp=5, maxlen: int = 10,
num_iterations=5000, num_chains=3, q=0.1,
alpha_pos=100, beta_pos=1,
alpha_neg=100, beta_neg=1,
alpha_l=None, beta_l=None,
discretization_method='randomforest', random_state=0):
'''
Params
------
n_rules
number of rules to be used in SA_patternbased and also the output of generate_rules
supp
The higher this supp, the 'larger' a pattern is. 5% is a generally good number
maxlen
maximum length of a pattern
num_iterations
number of iterations in each chain
num_chains
number of chains in the simulated annealing search algorithm
q
alpha_pos
$\rho = alpha/(alpha+beta)$. Make sure $\rho$ is close to one when choosing alpha and beta
The alpha and beta parameters alter the prior distributions for different rules
beta_pos
alpha_neg
beta_neg
alpha_l
beta_l
discretization_method
discretization method
'''
self.n_rules = n_rules
self.supp = supp
self.maxlen = maxlen
self.num_iterations = num_iterations
self.num_chains = num_chains
self.q = q
self.alpha_pos = alpha_pos
self.beta_pos = beta_pos
self.alpha_neg = alpha_neg
self.beta_neg = beta_neg
self.discretization_method = discretization_method
self.alpha_l = alpha_l
self.beta_l = beta_l
self.random_state = 0
| (self, n_rules: int = 2000, supp=5, maxlen: int = 10, num_iterations=5000, num_chains=3, q=0.1, alpha_pos=100, beta_pos=1, alpha_neg=100, beta_neg=1, alpha_l=None, beta_l=None, discretization_method='randomforest', random_state=0) | [
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16,715 | imodels.rule_set.brs | __str__ | null | def __str__(self):
return ' '.join(str(r) for r in self.rules_)
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16,718 | imodels.rule_set.brs | _compute_prob | null | def _compute_prob(self, rules, y):
Yhat = (np.sum(self.RMatrix[:, rules], axis=1) > 0).astype(int)
TP, FP, TN, FN = _get_confusion_matrix(Yhat, y)
Kn_count = list(np.bincount([self.rules_len_list[x] for x in rules], minlength=self.maxlen + 1))
prior_ChsRules = sum([_log_betabin(Kn_count[i], self.pattern_space[i], self.alpha_l[i], self.beta_l[i]) for i in
range(1, len(Kn_count), 1)])
likelihood_1 = _log_betabin(TP, TP + FP, self.alpha_pos, self.beta_pos)
likelihood_2 = _log_betabin(TN, FN + TN, self.alpha_neg, self.beta_neg)
return [TP, FP, TN, FN], [prior_ChsRules, likelihood_1, likelihood_2]
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16,719 | imodels.rule_set.rule_set | _eval_weighted_rule_sum | null | def _eval_weighted_rule_sum(self, X) -> np.ndarray:
check_is_fitted(self, ['rules_without_feature_names_', 'n_features_', 'feature_placeholders'])
X = check_array(X)
if X.shape[1] != self.n_features_:
raise ValueError("X.shape[1] = %d should be equal to %d, the number of features at training time."
" Please reshape your data."
% (X.shape[1], self.n_features_))
df = pd.DataFrame(X, columns=self.feature_placeholders)
selected_rules = self.rules_without_feature_names_
scores = np.zeros(X.shape[0])
for r in selected_rules:
features_r_uses = list(map(lambda x: x[0], r.agg_dict.keys()))
scores[df[features_r_uses].query(str(r)).index.values] += r.args[0]
return scores
| (self, X) -> numpy.ndarray | [
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16,720 | imodels.rule_set.rule_set | _extract_rules | null | def _extract_rules(self, X, y):
pass
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16,721 | imodels.rule_set.brs | _generate_rules | This function generates rules that satisfy supp and maxlen using fpgrowth, then it selects the top n_rules rules that make data have the biggest decrease in entropy.
There are two ways to generate rules. fpgrowth can handle cases where the maxlen is small. If maxlen<=3, fpgrowth can generates rules much faster than randomforest.
If maxlen is big, fpgrowth tends to generate too many rules that overflow the memory.
| def _generate_rules(self, X, y, verbose):
'''This function generates rules that satisfy supp and maxlen using fpgrowth, then it selects the top n_rules rules that make data have the biggest decrease in entropy.
There are two ways to generate rules. fpgrowth can handle cases where the maxlen is small. If maxlen<=3, fpgrowth can generates rules much faster than randomforest.
If maxlen is big, fpgrowth tends to generate too many rules that overflow the memory.
'''
df = 1 - X # df has negative associations
df.columns = [name.strip() + '_neg' for name in X.columns]
df = pd.concat([X, df], axis=1)
if self.discretization_method == 'fpgrowth' and self.maxlen <= 3:
itemMatrix = [[item for item in df.columns if row[item] == 1] for i, row in df.iterrows()]
pindex = np.where(y == 1)[0]
rules = fpgrowth([itemMatrix[i] for i in pindex], supp=self.supp, zmin=1, zmax=self.maxlen)
rules = [tuple(np.sort(rule[0])) for rule in rules]
rules = list(set(rules))
else:
'''todo: replace this with imodels.RFDiscretizer
'''
rules = []
for length in range(1, self.maxlen + 1, 1):
n_estimators = min(pow(df.shape[1], length), 4000)
clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=length)
clf.fit(X, y)
for n in range(n_estimators):
rules.extend(_extract_rules(clf.estimators_[n], df.columns))
rules = [list(x) for x in set(tuple(x) for x in rules)]
self.rules_ = rules
# select the top n_rules rules using secondary criteria, information gain
self._screen_rules(df, y, verbose) # updates self.rules_
self._set_pattern_space()
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16,722 | imodels.rule_set.rule_set | _get_complexity | null | def _get_complexity(self):
check_is_fitted(self, ['rules_without_feature_names_'])
return sum([len(rule.agg_dict) for rule in self.rules_without_feature_names_])
| (self) | [
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] |
16,727 | imodels.rule_set.brs | _normalize | null | def _normalize(self, rules_new):
try:
rules_len = [len(self.rules_[index]) for index in rules_new]
rules = [rules_new[i] for i in np.argsort(rules_len)[::-1][:len(rules_len)]]
p1 = 0
while p1 < len(rules):
for p2 in range(p1 + 1, len(rules), 1):
if set(self.rules_[rules[p2]]).issubset(set(self.rules_[rules[p1]])):
rules.remove(rules[p1])
p1 -= 1
break
p1 += 1
return rules
except:
return rules_new.copy()
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16,728 | imodels.rule_set.brs | _normalize_add | null | def _normalize_add(self, rules_new, rule_index):
rules = rules_new.copy()
for rule in rules_new:
if set(self.rules_[rule]).issubset(self.rules_[rule_index]):
return rules_new.copy()
if set(self.rules_[rule_index]).issubset(self.rules_[rule]):
rules.remove(rule)
rules.append(rule_index)
return rules
| (self, rules_new, rule_index) | [
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] |
16,729 | imodels.rule_set.brs | _print_rules | null | def _print_rules(self, rules_max):
for rule_index in rules_max:
print(self.rules_[rule_index])
| (self, rules_max) | [
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0.05576968193054199,
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16,730 | imodels.rule_set.brs | _propose | null | def _propose(self, rules_curr, rules_norm, q, y):
nRules = len(self.rules_)
yhat = (np.sum(self.RMatrix[:, rules_curr], axis=1) > 0).astype(int)
incorr = np.where(y != yhat)[0]
N = len(rules_curr)
if len(incorr) == 0:
# BOA correctly classified all points but there could be redundant patterns, so cleaning is needed
move = ['clean']
else:
ex = sample(incorr.tolist(), 1)[0]
t = random()
if y[ex] == 1 or N == 1:
if t < 1.0 / 2 or N == 1:
move = ['add'] # action: add
else:
move = ['cut', 'add'] # action: replace
else:
if t < 1.0 / 2:
move = ['cut'] # action: cut
else:
move = ['cut', 'add'] # action: replace
if move[0] == 'cut':
""" cut """
if random() < q:
candidate = list(set(np.where(self.RMatrix[ex, :] == 1)[0]).intersection(rules_curr))
if len(candidate) == 0:
candidate = rules_curr
cut_rule = sample(candidate, 1)[0]
else:
p = []
all_sum = np.sum(self.RMatrix[:, rules_curr], axis=1)
for index, rule in enumerate(rules_curr):
yhat = ((all_sum - np.array(self.RMatrix[:, rule])) > 0).astype(int)
TP, FP, TN, FN = _get_confusion_matrix(yhat, y)
p.append(TP.astype(float) / (TP + FP + 1))
p = [x - min(p) for x in p]
p = np.exp(p)
p = np.insert(p, 0, 0)
p = np.array(list(_accumulate(p)))
if p[-1] == 0:
index = sample(range(len(rules_curr)), 1)[0]
else:
p = p / p[-1]
index = _find_lt(p, random())
cut_rule = rules_curr[index]
rules_curr.remove(cut_rule)
rules_norm = self._normalize(rules_curr)
move.remove('cut')
if len(move) > 0 and move[0] == 'add':
""" add """
if random() < q:
add_rule = sample(range(nRules), 1)[0]
else:
Yhat_neg_index = list(np.where(np.sum(self.RMatrix[:, rules_curr], axis=1) < 1)[0])
mat = np.multiply(self.RMatrix[Yhat_neg_index, :].transpose(), y[Yhat_neg_index])
TP = np.sum(mat, axis=1)
FP = np.array((np.sum(self.RMatrix[Yhat_neg_index, :], axis=0) - TP))
p = (TP.astype(float) / (TP + FP + 1))
p[rules_curr] = 0
add_rule = sample(np.where(p == max(p))[0].tolist(), 1)[0]
if add_rule not in rules_curr:
rules_curr.append(add_rule)
rules_norm = self._normalize(rules_curr)
if len(move) > 0 and move[0] == 'clean':
remove = []
for i, rule in enumerate(rules_norm):
yhat = (np.sum(
self.RMatrix[:, [rule for j, rule in enumerate(rules_norm) if (j != i and j not in remove)]],
axis=1) > 0).astype(int)
TP, FP, TN, FN = _get_confusion_matrix(yhat, y)
if TP + FP == 0:
remove.append(i)
for x in remove:
rules_norm.remove(x)
return rules_curr, rules_norm
return rules_curr, rules_norm
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16,731 | imodels.rule_set.rule_set | _prune_rules | null | def _prune_rules(self, rules):
pass
| (self, rules) | [
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16,734 | imodels.rule_set.rule_set | _score_rules | null | def _score_rules(self, X, y, rules):
pass
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16,735 | imodels.rule_set.brs | _screen_rules | Screening rules using information gain
| def _screen_rules(self, df, y, verbose):
'''Screening rules using information gain
'''
item_ind_dict = {}
for i, name in enumerate(df.columns):
item_ind_dict[name] = i
indices = np.array(
list(itertools.chain.from_iterable([[
item_ind_dict[x] for x in rule]
for rule in self.rules_])))
len_rules = [len(rule) for rule in self.rules_]
indptr = list(_accumulate(len_rules))
indptr.insert(0, 0)
indptr = np.array(indptr)
data = np.ones(len(indices))
rule_matrix = csc_matrix((data, indices, indptr),
shape=(len(df.columns),
len(self.rules_)))
mat = df.values @ rule_matrix
print('mat.shape', mat.shape)
len_matrix = np.array([len_rules] * df.shape[0])
Z = (mat == len_matrix).astype(int)
Zpos = [Z[i] for i in np.where(y > 0)][0]
TP = np.sum(Zpos, axis=0)
supp_select = np.where(TP >= self.supp * sum(y) / 100)[0]
FP = np.sum(Z, axis=0) - TP
TN = len(y) - np.sum(y) - FP
FN = np.sum(y) - TP
p1 = TP.astype(float) / (TP + FP)
p2 = FN.astype(float) / (FN + TN)
pp = (TP + FP).astype(float) / (TP + FP + TN + FN)
# p1 = np.clip(p1, a_min=1e-10, a_max=1-1e-10)
print('\n\n\n\np1.shape', p1.shape, 'pp.shape', pp.shape, 'cond_entropy.shape') # , cond_entropy.shape)
with warnings.catch_warnings():
if not verbose:
warnings.simplefilter("ignore") # ignore warnings about invalid values (e.g. log(0))
cond_entropy = -pp * (p1 * np.log(p1) + (1 - p1) * np.log(1 - p1)) - (1 - pp) * (
p2 * np.log(p2) + (1 - p2) * np.log(1 - p2))
cond_entropy[p1 * (1 - p1) == 0] = -((1 - pp) * (p2 * np.log(p2) + (1 - p2) * np.log(1 - p2)))[
p1 * (1 - p1) == 0]
cond_entropy[p2 * (1 - p2) == 0] = -(pp * (p1 * np.log(p1) + (1 - p1) * np.log(1 - p1)))[p2 * (1 - p2) == 0]
cond_entropy[p1 * (1 - p1) * p2 * (1 - p2) == 0] = 0
select = np.argsort(cond_entropy[supp_select])[::-1][-self.n_rules:]
self.rules_ = [self.rules_[i] for i in supp_select[select]]
self.RMatrix = np.array(Z[:, supp_select[select]])
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16,736 | imodels.rule_set.brs | _set_pattern_space | Compute the rule space from the levels in each attribute
| def _set_pattern_space(self):
"""Compute the rule space from the levels in each attribute
"""
# add feat_neg to each existing feature feat
for item in self.attr_names:
self.attr_level_num[item + '_neg'] = self.attr_level_num[item]
tmp = [item + '_neg' for item in self.attr_names]
self.attr_names.extend(tmp)
# set up pattern_space
self.pattern_space = np.zeros(self.maxlen + 1)
for k in range(1, self.maxlen + 1, 1):
for subset in combinations(self.attr_names, k):
tmp = 1
for i in subset:
tmp = tmp * self.attr_level_num[i]
# print('subset', subset, 'tmp', tmp, 'k', k)
self.pattern_space[k] = self.pattern_space[k] + tmp
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] |
16,739 | imodels.rule_set.brs | fit |
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array_like, shape = [n_samples]
Labels
feature_names : array_like, shape = [n_features], optional (default: [])
String labels for each feature.
If empty and X is a DataFrame, column labels are used.
If empty and X is not a DataFrame, then features are simply enumerated
| def fit(self, X, y, feature_names: list = None, init=[], verbose=False):
'''
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array_like, shape = [n_samples]
Labels
feature_names : array_like, shape = [n_features], optional (default: [])
String labels for each feature.
If empty and X is a DataFrame, column labels are used.
If empty and X is not a DataFrame, then features are simply enumerated
'''
# check inputs
self.attr_level_num = defaultdict(int) # any missing value defaults to 0
self.attr_names = []
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
np.random.seed(self.random_state)
# convert to pandas DataFrame
X = pd.DataFrame(X, columns=feature_names)
for i, name in enumerate(X.columns):
self.attr_level_num[name] += 1
self.attr_names.append(name)
self.attr_names_orig = deepcopy(self.attr_names)
self.attr_names = list(set(self.attr_names))
# set up patterns
self._set_pattern_space()
# parameter checking
if self.alpha_l is None or self.beta_l is None or len(self.alpha_l) != self.maxlen or len(
self.beta_l) != self.maxlen:
if verbose:
print('No or wrong input for alpha_l and beta_l - the model will use default parameters.')
self.C = [1.0 / self.maxlen] * self.maxlen
self.C.insert(0, -1)
self.alpha_l = [10] * (self.maxlen + 1)
self.beta_l = [10 * self.pattern_space[i] / self.C[i] for i in range(self.maxlen + 1)]
else:
self.alpha_l = [1] + list(self.alpha_l)
self.beta_l = [1] + list(self.beta_l)
# setup
self._generate_rules(X, y, verbose)
n_rules_current = len(self.rules_)
self.rules_len_list = [len(rule) for rule in self.rules_]
maps = defaultdict(list)
T0 = 1000 # initial temperature for simulated annealing
split = 0.7 * self.num_iterations
# run simulated annealing
for chain in range(self.num_chains):
# initialize with a random pattern set
if init != []:
rules_curr = init.copy()
else:
assert n_rules_current > 1, f'Only {n_rules_current} potential rules found, change hyperparams to allow for more'
N = sample(range(1, min(8, n_rules_current), 1), 1)[0]
rules_curr = sample(range(n_rules_current), N)
rules_curr_norm = self._normalize(rules_curr)
pt_curr = -100000000000
maps[chain].append(
[-1, [pt_curr / 3, pt_curr / 3, pt_curr / 3], rules_curr, [self.rules_[i] for i in rules_curr]])
for iter in range(self.num_iterations):
if iter >= split:
p = np.array(range(1 + len(maps[chain])))
p = np.array(list(_accumulate(p)))
p = p / p[-1]
index = _find_lt(p, random())
rules_curr = maps[chain][index][2].copy()
rules_curr_norm = maps[chain][index][2].copy()
# propose new rules
rules_new, rules_norm = self._propose(rules_curr.copy(), rules_curr_norm.copy(), self.q, y)
# compute probability of new rules
cfmatrix, prob = self._compute_prob(rules_new, y)
T = T0 ** (1 - iter / self.num_iterations) # temperature for simulated annealing
pt_new = sum(prob)
with warnings.catch_warnings():
if not verbose:
warnings.simplefilter("ignore")
alpha = np.exp(float(pt_new - pt_curr) / T)
if pt_new > sum(maps[chain][-1][1]):
maps[chain].append([iter, prob, rules_new, [self.rules_[i] for i in rules_new]])
if verbose:
print((
'\n** chain = {}, max at iter = {} ** \n accuracy = {}, TP = {},FP = {}, TN = {}, FN = {}'
'\n pt_new is {}, prior_ChsRules={}, likelihood_1 = {}, likelihood_2 = {}\n').format(
chain, iter, (cfmatrix[0] + cfmatrix[2] + 0.0) / len(y), cfmatrix[0], cfmatrix[1],
cfmatrix[2], cfmatrix[3], sum(prob), prob[0], prob[1], prob[2])
)
self._print_rules(rules_new)
print(rules_new)
if random() <= alpha:
rules_curr_norm, rules_curr, pt_curr = rules_norm.copy(), rules_new.copy(), pt_new
pt_max = [sum(maps[chain][-1][1]) for chain in range(self.num_chains)]
index = pt_max.index(max(pt_max))
self.rules_ = maps[index][-1][3]
return self
| (self, X, y, feature_names: Optional[list] = None, init=[], verbose=False) | [
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16,742 | imodels.rule_set.brs | predict | null | def predict(self, X):
check_is_fitted(self)
if isinstance(X, np.ndarray):
df = pd.DataFrame(X, columns=self.attr_names_orig)
else:
df = X
Z = [[]] * len(self.rules_)
dfn = 1 - df # df has negative associations
dfn.columns = [name.strip() + '_neg' for name in df.columns]
df = pd.concat([df, dfn], axis=1)
for i, rule in enumerate(self.rules_):
Z[i] = (np.sum(df[list(rule)], axis=1) == len(rule)).astype(int)
Yhat = (np.sum(Z, axis=0) > 0).astype(int)
return Yhat
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16,743 | imodels.rule_set.brs | predict_proba | null | def predict_proba(self, X):
raise Exception('BOA does not support predicted probabilities.')
| (self, X) | [
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16,745 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` 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 ``fit`` 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 ``fit``.
- ``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
----------
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
init : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``init`` parameter in ``fit``.
verbose : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``verbose`` parameter in ``fit``.
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.rule_set.brs.BayesianRuleSetClassifier, *, feature_names: Union[bool, NoneType, str] = '$UNCHANGED$', init: Union[bool, NoneType, str] = '$UNCHANGED$', verbose: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_set.brs.BayesianRuleSetClassifier | [
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] |
16,748 | imodels.rule_set.boosted_rules | BoostedRulesClassifier | An easy-interpretable classifier optimizing simple logical rules.
Params
------
estimator: object with fit and predict methods
Defaults to DecisionTreeClassifier with AdaBoost.
For SLIPPER, should pass estimator=imodels.SlipperBaseEstimator
| class BoostedRulesClassifier(AdaBoostClassifier):
'''An easy-interpretable classifier optimizing simple logical rules.
Params
------
estimator: object with fit and predict methods
Defaults to DecisionTreeClassifier with AdaBoost.
For SLIPPER, should pass estimator=imodels.SlipperBaseEstimator
'''
def __init__(
self,
estimator=DecisionTreeClassifier(max_depth=1),
*,
n_estimators=15,
learning_rate=1.0,
random_state=None,
):
try: # sklearn version >= 1.2
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
except: # sklearn version < 1.2
super().__init__(
base_estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.estimator = estimator
def fit(self, X, y, feature_names=None, **kwargs):
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
super().fit(X, y, **kwargs)
self.complexity_ = len(self.estimators_)
| (estimator=DecisionTreeClassifier(max_depth=1), *, n_estimators=15, learning_rate=1.0, random_state=None) | [
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16,751 | imodels.rule_set.boosted_rules | __init__ | null | def __init__(
self,
estimator=DecisionTreeClassifier(max_depth=1),
*,
n_estimators=15,
learning_rate=1.0,
random_state=None,
):
try: # sklearn version >= 1.2
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
except: # sklearn version < 1.2
super().__init__(
base_estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.estimator = estimator
| (self, estimator=DecisionTreeClassifier(max_depth=1), *, n_estimators=15, learning_rate=1.0, random_state=None) | [
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16,775 | imodels.rule_set.boosted_rules | fit | null | def fit(self, X, y, feature_names=None, **kwargs):
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
super().fit(X, y, **kwargs)
self.complexity_ = len(self.estimators_)
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] |
16,782 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` 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 ``fit`` 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 ``fit``.
- ``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
----------
feature_names : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``feature_names`` parameter in ``fit``.
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.rule_set.boosted_rules.BoostedRulesClassifier, *, feature_names: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.rule_set.boosted_rules.BoostedRulesClassifier | [
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] |
16,789 | imodels.rule_set.boosted_rules | BoostedRulesRegressor | An easy-interpretable regressor optimizing simple logical rules.
Params
------
estimator: object with fit and predict methods
Defaults to DecisionTreeRegressor with AdaBoost.
| class BoostedRulesRegressor(AdaBoostRegressor):
'''An easy-interpretable regressor optimizing simple logical rules.
Params
------
estimator: object with fit and predict methods
Defaults to DecisionTreeRegressor with AdaBoost.
'''
def __init__(
self,
estimator=DecisionTreeRegressor(max_depth=1),
*,
n_estimators=15,
learning_rate=1.0,
random_state=13,
):
try: # sklearn version >= 1.2
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
except: # sklearn version < 1.2
super().__init__(
base_estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.estimator = estimator
def fit(self, X, y, feature_names=None, **kwargs):
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
super().fit(X, y, **kwargs)
self.complexity_ = len(self.estimators_)
| (estimator=DecisionTreeRegressor(max_depth=1), *, n_estimators=15, learning_rate=1.0, random_state=13) | [
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16,792 | imodels.rule_set.boosted_rules | __init__ | null | def __init__(
self,
estimator=DecisionTreeRegressor(max_depth=1),
*,
n_estimators=15,
learning_rate=1.0,
random_state=13,
):
try: # sklearn version >= 1.2
super().__init__(
estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
except: # sklearn version < 1.2
super().__init__(
base_estimator=estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state,
)
self.estimator = estimator
| (self, estimator=DecisionTreeRegressor(max_depth=1), *, n_estimators=15, learning_rate=1.0, random_state=13) | [
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16,823 | imodels.tree.c45_tree.c45_tree | C45TreeClassifier | A C4.5 tree classifier.
Parameters
----------
max_rules : int, optional (default=None)
Maximum number of split nodes allowed in the tree
| class C45TreeClassifier(BaseEstimator, ClassifierMixin):
"""A C4.5 tree classifier.
Parameters
----------
max_rules : int, optional (default=None)
Maximum number of split nodes allowed in the tree
"""
def __init__(self, max_rules: int = None):
super().__init__()
self.max_rules = max_rules
def fit(self, X, y, feature_names: str = None):
self.complexity_ = 0
# X, y = check_X_y(X, y)
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
self.resultType = type(y[0])
if feature_names is None:
self.feature_names = [f'X_{x}' for x in range(X.shape[1])]
else:
# only include alphanumeric chars / replace spaces with underscores
self.feature_names = [''.join([i for i in x if i.isalnum()]).replace(' ', '_')
for x in feature_names]
self.feature_names = [
'X_' + x if x[0].isdigit()
else x
for x in self.feature_names
]
assert len(self.feature_names) == X.shape[1]
data = [[] for i in range(len(self.feature_names))]
categories = []
for i in range(len(X)):
categories.append(str(y[i]))
for j in range(len(self.feature_names)):
data[j].append(X[i][j])
root = ET.Element('GreedyTree')
self.grow_tree(data, categories, root, self.feature_names) # adds to root
self.tree_ = ET.tostring(root, encoding="unicode")
# print('self.tree_', self.tree_)
self.dom_ = minidom.parseString(self.tree_)
return self
def impute_nodes(self, X, y):
"""
Returns
---
the leaf by which this sample would be classified
"""
source_node = self.root
for i in range(len(y)):
sample, label = X[i, ...], y[i]
_add_label(source_node, label)
nodes = [source_node]
while len(nodes) > 0:
node = nodes.pop()
if not node.hasChildNodes():
continue
else:
att_name = node.firstChild.nodeName
if att_name != "#text":
att = sample[self.feature_names.index(att_name)]
next_node = _get_next_node(node.childNodes, att)
else:
next_node = node.firstChild
_add_label(next_node, label)
nodes.append(next_node)
self._calc_probs(source_node)
# self.dom_.childNodes[0] = source_node
# self.tree_.source = source_node
def _calc_probs(self, node):
node.nodeValue = np.mean(node.labels)
if not node.hasChildNodes():
return
for c in node.childNodes:
self._calc_probs(c)
def raw_preds(self, X):
check_is_fitted(self, ['tree_', 'resultType', 'feature_names'])
X = check_array(X)
if isinstance(X, pd.DataFrame):
X = deepcopy(X)
X.columns = self.feature_names
root = self.root
prediction = []
for i in range(X.shape[0]):
answerlist = decision(root, X[i], self.feature_names, 1)
answerlist = sorted(answerlist.items(), key=lambda x: x[1], reverse=True)
answer = answerlist[0][0]
# prediction.append(self.resultType(answer))
prediction.append(float(answer))
return np.array(prediction)
def predict(self, X):
raw_preds = self.raw_preds(X)
return (raw_preds > np.ones_like(raw_preds) * 0.5).astype(int)
def predict_proba(self, X):
raw_preds = self.raw_preds(X)
return np.vstack((1 - raw_preds, raw_preds)).transpose()
def __str__(self):
check_is_fitted(self, ['tree_'])
return self.dom_.toprettyxml(newl="\r\n")
def grow_tree(self, X_t: List[list], y_str: List[str], parent, attrs_names):
"""
Parameters
----------
X_t: List[list]
input data transposed (num_features x num_observations)
y_str: List[str]
outcome represented as strings
parent
attrs_names
"""
# check that y contains more than 1 distinct value
if len(set(y_str)) > 1:
split = []
# loop over features and build up potential splits
for i in range(len(X_t)):
if set(X_t[i]) == set("?"):
split.append(0)
else:
if is_numeric_feature(X_t[i]):
split.append(gain(y_str, X_t[i]))
else:
split.append(gain_ratio(y_str, X_t[i]))
# no good split, return child node
if max(split) == 0:
set_as_leaf_node(parent, y_str)
# there is a good split
else:
index_selected = split.index(max(split))
name_selected = str(attrs_names[index_selected])
self.complexity_ += 1
if is_numeric_feature(X_t[index_selected]):
# split on this point
split_point = get_best_split(y_str, X_t[index_selected])
# build up children nodes
r_child_X = [[] for i in range(len(X_t))]
r_child_y = []
l_child_X = [[] for i in range(len(X_t))]
l_child_y = []
for i in range(len(y_str)):
if not X_t[index_selected][i] == "?":
if float(X_t[index_selected][i]) < float(split_point):
l_child_y.append(y_str[i])
for j in range(len(X_t)):
l_child_X[j].append(X_t[j][i])
else:
r_child_y.append(y_str[i])
for j in range(len(X_t)):
r_child_X[j].append(X_t[j][i])
# grow child nodes as well
if len(l_child_y) > 0 and len(r_child_y) > 0 and (
self.max_rules is None or
self.complexity_ <= self.max_rules
):
p_l = float(len(l_child_y)) / (len(X_t[index_selected]) - X_t[index_selected].count("?"))
son = ET.SubElement(parent, name_selected,
{'feature': str(split_point), "flag": "l", "p": str(round(p_l, 3))})
self.grow_tree(l_child_X, l_child_y, son, attrs_names)
son = ET.SubElement(parent, name_selected,
{'feature': str(split_point), "flag": "r", "p": str(round(1 - p_l, 3))})
self.grow_tree(r_child_X, r_child_y, son, attrs_names)
else:
num_max = 0
for cat in set(y_str):
num_cat = y_str.count(cat)
if num_cat > num_max:
num_max = num_cat
most_cat = cat
parent.text = most_cat
else:
# split on non-numeric variable (e.g. categorical)
# create a leaf for each unique value
for k in set(X_t[index_selected]):
if not k == "?" and (
self.max_rules is None or
self.complexity_ <= self.max_rules
):
child_X = [[] for i in range(len(X_t))]
child_y = []
for i in range(len(y_str)):
if X_t[index_selected][i] == k:
child_y.append(y_str[i])
for j in range(len(X_t)):
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16,825 | imodels.tree.c45_tree.c45_tree | __init__ | null | def __init__(self, max_rules: int = None):
super().__init__()
self.max_rules = max_rules
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16,829 | imodels.tree.c45_tree.c45_tree | __str__ | null | def __str__(self):
check_is_fitted(self, ['tree_'])
return self.dom_.toprettyxml(newl="\r\n")
| (self) | [
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16,830 | imodels.tree.c45_tree.c45_tree | _calc_probs | null | def _calc_probs(self, node):
node.nodeValue = np.mean(node.labels)
if not node.hasChildNodes():
return
for c in node.childNodes:
self._calc_probs(c)
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16,841 | imodels.tree.c45_tree.c45_tree | fit | null | def fit(self, X, y, feature_names: str = None):
self.complexity_ = 0
# X, y = check_X_y(X, y)
X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
self.resultType = type(y[0])
if feature_names is None:
self.feature_names = [f'X_{x}' for x in range(X.shape[1])]
else:
# only include alphanumeric chars / replace spaces with underscores
self.feature_names = [''.join([i for i in x if i.isalnum()]).replace(' ', '_')
for x in feature_names]
self.feature_names = [
'X_' + x if x[0].isdigit()
else x
for x in self.feature_names
]
assert len(self.feature_names) == X.shape[1]
data = [[] for i in range(len(self.feature_names))]
categories = []
for i in range(len(X)):
categories.append(str(y[i]))
for j in range(len(self.feature_names)):
data[j].append(X[i][j])
root = ET.Element('GreedyTree')
self.grow_tree(data, categories, root, self.feature_names) # adds to root
self.tree_ = ET.tostring(root, encoding="unicode")
# print('self.tree_', self.tree_)
self.dom_ = minidom.parseString(self.tree_)
return self
| (self, X, y, feature_names: Optional[str] = None) | [
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] |
16,844 | imodels.tree.c45_tree.c45_tree | grow_tree |
Parameters
----------
X_t: List[list]
input data transposed (num_features x num_observations)
y_str: List[str]
outcome represented as strings
parent
attrs_names
| def grow_tree(self, X_t: List[list], y_str: List[str], parent, attrs_names):
"""
Parameters
----------
X_t: List[list]
input data transposed (num_features x num_observations)
y_str: List[str]
outcome represented as strings
parent
attrs_names
"""
# check that y contains more than 1 distinct value
if len(set(y_str)) > 1:
split = []
# loop over features and build up potential splits
for i in range(len(X_t)):
if set(X_t[i]) == set("?"):
split.append(0)
else:
if is_numeric_feature(X_t[i]):
split.append(gain(y_str, X_t[i]))
else:
split.append(gain_ratio(y_str, X_t[i]))
# no good split, return child node
if max(split) == 0:
set_as_leaf_node(parent, y_str)
# there is a good split
else:
index_selected = split.index(max(split))
name_selected = str(attrs_names[index_selected])
self.complexity_ += 1
if is_numeric_feature(X_t[index_selected]):
# split on this point
split_point = get_best_split(y_str, X_t[index_selected])
# build up children nodes
r_child_X = [[] for i in range(len(X_t))]
r_child_y = []
l_child_X = [[] for i in range(len(X_t))]
l_child_y = []
for i in range(len(y_str)):
if not X_t[index_selected][i] == "?":
if float(X_t[index_selected][i]) < float(split_point):
l_child_y.append(y_str[i])
for j in range(len(X_t)):
l_child_X[j].append(X_t[j][i])
else:
r_child_y.append(y_str[i])
for j in range(len(X_t)):
r_child_X[j].append(X_t[j][i])
# grow child nodes as well
if len(l_child_y) > 0 and len(r_child_y) > 0 and (
self.max_rules is None or
self.complexity_ <= self.max_rules
):
p_l = float(len(l_child_y)) / (len(X_t[index_selected]) - X_t[index_selected].count("?"))
son = ET.SubElement(parent, name_selected,
{'feature': str(split_point), "flag": "l", "p": str(round(p_l, 3))})
self.grow_tree(l_child_X, l_child_y, son, attrs_names)
son = ET.SubElement(parent, name_selected,
{'feature': str(split_point), "flag": "r", "p": str(round(1 - p_l, 3))})
self.grow_tree(r_child_X, r_child_y, son, attrs_names)
else:
num_max = 0
for cat in set(y_str):
num_cat = y_str.count(cat)
if num_cat > num_max:
num_max = num_cat
most_cat = cat
parent.text = most_cat
else:
# split on non-numeric variable (e.g. categorical)
# create a leaf for each unique value
for k in set(X_t[index_selected]):
if not k == "?" and (
self.max_rules is None or
self.complexity_ <= self.max_rules
):
child_X = [[] for i in range(len(X_t))]
child_y = []
for i in range(len(y_str)):
if X_t[index_selected][i] == k:
child_y.append(y_str[i])
for j in range(len(X_t)):
child_X[j].append(X_t[j][i])
son = ET.SubElement(parent, name_selected, {
'feature': k, "flag": "m",
'p': str(round(
float(len(child_y)) / (
len(X_t[index_selected]) - X_t[index_selected].count("?")),
3))})
self.grow_tree(child_X, child_y, son, attrs_names)
else:
parent.text = y_str[0]
| (self, X_t: List[list], y_str: List[str], parent, attrs_names) | [
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16,845 | imodels.tree.c45_tree.c45_tree | impute_nodes |
Returns
---
the leaf by which this sample would be classified
| def impute_nodes(self, X, y):
"""
Returns
---
the leaf by which this sample would be classified
"""
source_node = self.root
for i in range(len(y)):
sample, label = X[i, ...], y[i]
_add_label(source_node, label)
nodes = [source_node]
while len(nodes) > 0:
node = nodes.pop()
if not node.hasChildNodes():
continue
else:
att_name = node.firstChild.nodeName
if att_name != "#text":
att = sample[self.feature_names.index(att_name)]
next_node = _get_next_node(node.childNodes, att)
else:
next_node = node.firstChild
_add_label(next_node, label)
nodes.append(next_node)
self._calc_probs(source_node)
# self.dom_.childNodes[0] = source_node
# self.tree_.source = source_node
| (self, X, y) | [
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16,846 | imodels.tree.c45_tree.c45_tree | predict | null | def predict(self, X):
raw_preds = self.raw_preds(X)
return (raw_preds > np.ones_like(raw_preds) * 0.5).astype(int)
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16,847 | imodels.tree.c45_tree.c45_tree | predict_proba | null | def predict_proba(self, X):
raw_preds = self.raw_preds(X)
return np.vstack((1 - raw_preds, raw_preds)).transpose()
| (self, X) | [
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16,848 | imodels.tree.c45_tree.c45_tree | raw_preds | null | def raw_preds(self, X):
check_is_fitted(self, ['tree_', 'resultType', 'feature_names'])
X = check_array(X)
if isinstance(X, pd.DataFrame):
X = deepcopy(X)
X.columns = self.feature_names
root = self.root
prediction = []
for i in range(X.shape[0]):
answerlist = decision(root, X[i], self.feature_names, 1)
answerlist = sorted(answerlist.items(), key=lambda x: x[1], reverse=True)
answer = answerlist[0][0]
# prediction.append(self.resultType(answer))
prediction.append(float(answer))
return np.array(prediction)
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16,853 | sklearn.base | ClassifierMixin | Mixin class for all classifiers in scikit-learn.
This mixin defines the following functionality:
- `_estimator_type` class attribute defaulting to `"classifier"`;
- `score` method that default to :func:`~sklearn.metrics.accuracy_score`.
- enforce that `fit` requires `y` to be passed through the `requires_y` tag.
Read more in the :ref:`User Guide <rolling_your_own_estimator>`.
Examples
--------
>>> import numpy as np
>>> from sklearn.base import BaseEstimator, ClassifierMixin
>>> # Mixin classes should always be on the left-hand side for a correct MRO
>>> class MyEstimator(ClassifierMixin, BaseEstimator):
... def __init__(self, *, param=1):
... self.param = param
... def fit(self, X, y=None):
... self.is_fitted_ = True
... return self
... def predict(self, X):
... return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=1)
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([1, 1, 1])
>>> estimator.score(X, y)
0.66...
| class ClassifierMixin:
"""Mixin class for all classifiers in scikit-learn.
This mixin defines the following functionality:
- `_estimator_type` class attribute defaulting to `"classifier"`;
- `score` method that default to :func:`~sklearn.metrics.accuracy_score`.
- enforce that `fit` requires `y` to be passed through the `requires_y` tag.
Read more in the :ref:`User Guide <rolling_your_own_estimator>`.
Examples
--------
>>> import numpy as np
>>> from sklearn.base import BaseEstimator, ClassifierMixin
>>> # Mixin classes should always be on the left-hand side for a correct MRO
>>> class MyEstimator(ClassifierMixin, BaseEstimator):
... def __init__(self, *, param=1):
... self.param = param
... def fit(self, X, y=None):
... self.is_fitted_ = True
... return self
... def predict(self, X):
... return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=1)
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([1, 1, 1])
>>> estimator.score(X, y)
0.66...
"""
_estimator_type = "classifier"
def score(self, X, y, sample_weight=None):
"""
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for `X`.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
score : float
Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
"""
from .metrics import accuracy_score
return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
def _more_tags(self):
return {"requires_y": True}
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16,856 | imodels.tree.cart_ccp | DecisionTreeCCPClassifier | null | class DecisionTreeCCPClassifier(DecisionTreeClassifier):
def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args,
**kwargs):
self.desired_complexity = desired_complexity
# print('est', estimator_)
self.estimator_ = estimator_
self.complexity_measure = complexity_measure
def _get_alpha(self, X, y, sample_weight=None, *args, **kwargs):
path = self.estimator_.cost_complexity_pruning_path(X, y)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
complexities = {}
low = 0
high = len(ccp_alphas) - 1
cur = 0
while low <= high:
cur = (high + low) // 2
est_params = self.estimator_.get_params()
est_params['ccp_alpha'] = ccp_alphas[cur]
copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
copied_estimator.fit(X, y)
if self._get_complexity(copied_estimator, self.complexity_measure) < self.desired_complexity:
high = cur - 1
elif self._get_complexity(copied_estimator, self.complexity_measure) > self.desired_complexity:
low = cur + 1
else:
break
self.alpha = ccp_alphas[cur]
# for alpha in ccp_alphas:
# est_params = self.estimator_.get_params()
# est_params['ccp_alpha'] = alpha
# copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
# copied_estimator.fit(X, y)
# complexities[alpha] = self._get_complexity(copied_estimator,self.complexity_measure)
# closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
# self.alpha = closest_alpha
def fit(self, X, y, sample_weight=None, *args, **kwargs):
params_for_fitting = self.estimator_.get_params()
self._get_alpha(X, y, sample_weight, *args, **kwargs)
params_for_fitting['ccp_alpha'] = self.alpha
self.estimator_.set_params(**params_for_fitting)
self.estimator_.fit(X, y, *args, **kwargs)
def _get_complexity(self, BaseEstimator, complexity_measure):
return compute_tree_complexity(BaseEstimator.tree_, complexity_measure)
def predict_proba(self, *args, **kwargs):
if hasattr(self.estimator_, 'predict_proba'):
return self.estimator_.predict_proba(*args, **kwargs)
else:
return NotImplemented
def predict(self, X, *args, **kwargs):
return self.estimator_.predict(X, *args, **kwargs)
def score(self, *args, **kwargs):
if hasattr(self.estimator_, 'score'):
return self.estimator_.score(*args, **kwargs)
else:
return NotImplemented
| (estimator_: sklearn.base.BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args, **kwargs) | [
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16,858 | imodels.tree.cart_ccp | __init__ | null | def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args,
**kwargs):
self.desired_complexity = desired_complexity
# print('est', estimator_)
self.estimator_ = estimator_
self.complexity_measure = complexity_measure
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16,864 | sklearn.tree._classes | _compute_missing_values_in_feature_mask | Return boolean mask denoting if there are missing values for each feature.
This method also ensures that X is finite.
Parameter
---------
X : array-like of shape (n_samples, n_features), dtype=DOUBLE
Input data.
estimator_name : str or None, default=None
Name to use when raising an error. Defaults to the class name.
Returns
-------
missing_values_in_feature_mask : ndarray of shape (n_features,), or None
Missing value mask. If missing values are not supported or there
are no missing values, return None.
| def _compute_missing_values_in_feature_mask(self, X, estimator_name=None):
"""Return boolean mask denoting if there are missing values for each feature.
This method also ensures that X is finite.
Parameter
---------
X : array-like of shape (n_samples, n_features), dtype=DOUBLE
Input data.
estimator_name : str or None, default=None
Name to use when raising an error. Defaults to the class name.
Returns
-------
missing_values_in_feature_mask : ndarray of shape (n_features,), or None
Missing value mask. If missing values are not supported or there
are no missing values, return None.
"""
estimator_name = estimator_name or self.__class__.__name__
common_kwargs = dict(estimator_name=estimator_name, input_name="X")
if not self._support_missing_values(X):
assert_all_finite(X, **common_kwargs)
return None
with np.errstate(over="ignore"):
overall_sum = np.sum(X)
if not np.isfinite(overall_sum):
# Raise a ValueError in case of the presence of an infinite element.
_assert_all_finite_element_wise(X, xp=np, allow_nan=True, **common_kwargs)
# If the sum is not nan, then there are no missing values
if not np.isnan(overall_sum):
return None
missing_values_in_feature_mask = _any_isnan_axis0(X)
return missing_values_in_feature_mask
| (self, X, estimator_name=None) | [
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16,865 | sklearn.tree._classes | _fit | null | def _fit(
self,
X,
y,
sample_weight=None,
check_input=True,
missing_values_in_feature_mask=None,
):
random_state = check_random_state(self.random_state)
if check_input:
# Need to validate separately here.
# We can't pass multi_output=True because that would allow y to be
# csr.
# _compute_missing_values_in_feature_mask will check for finite values and
# compute the missing mask if the tree supports missing values
check_X_params = dict(
dtype=DTYPE, accept_sparse="csc", force_all_finite=False
)
check_y_params = dict(ensure_2d=False, dtype=None)
X, y = self._validate_data(
X, y, validate_separately=(check_X_params, check_y_params)
)
missing_values_in_feature_mask = (
self._compute_missing_values_in_feature_mask(X)
)
if issparse(X):
X.sort_indices()
if X.indices.dtype != np.intc or X.indptr.dtype != np.intc:
raise ValueError(
"No support for np.int64 index based sparse matrices"
)
if self.criterion == "poisson":
if np.any(y < 0):
raise ValueError(
"Some value(s) of y are negative which is"
" not allowed for Poisson regression."
)
if np.sum(y) <= 0:
raise ValueError(
"Sum of y is not positive which is "
"necessary for Poisson regression."
)
# Determine output settings
n_samples, self.n_features_in_ = X.shape
is_classification = is_classifier(self)
y = np.atleast_1d(y)
expanded_class_weight = None
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
if is_classification:
check_classification_targets(y)
y = np.copy(y)
self.classes_ = []
self.n_classes_ = []
if self.class_weight is not None:
y_original = np.copy(y)
y_encoded = np.zeros(y.shape, dtype=int)
for k in range(self.n_outputs_):
classes_k, y_encoded[:, k] = np.unique(y[:, k], return_inverse=True)
self.classes_.append(classes_k)
self.n_classes_.append(classes_k.shape[0])
y = y_encoded
if self.class_weight is not None:
expanded_class_weight = compute_sample_weight(
self.class_weight, y_original
)
self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
max_depth = np.iinfo(np.int32).max if self.max_depth is None else self.max_depth
if isinstance(self.min_samples_leaf, numbers.Integral):
min_samples_leaf = self.min_samples_leaf
else: # float
min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples))
if isinstance(self.min_samples_split, numbers.Integral):
min_samples_split = self.min_samples_split
else: # float
min_samples_split = int(ceil(self.min_samples_split * n_samples))
min_samples_split = max(2, min_samples_split)
min_samples_split = max(min_samples_split, 2 * min_samples_leaf)
if isinstance(self.max_features, str):
if self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_in_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_in_)))
elif self.max_features is None:
max_features = self.n_features_in_
elif isinstance(self.max_features, numbers.Integral):
max_features = self.max_features
else: # float
if self.max_features > 0.0:
max_features = max(1, int(self.max_features * self.n_features_in_))
else:
max_features = 0
self.max_features_ = max_features
max_leaf_nodes = -1 if self.max_leaf_nodes is None else self.max_leaf_nodes
if len(y) != n_samples:
raise ValueError(
"Number of labels=%d does not match number of samples=%d"
% (len(y), n_samples)
)
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X, DOUBLE)
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
# Set min_weight_leaf from min_weight_fraction_leaf
if sample_weight is None:
min_weight_leaf = self.min_weight_fraction_leaf * n_samples
else:
min_weight_leaf = self.min_weight_fraction_leaf * np.sum(sample_weight)
# Build tree
criterion = self.criterion
if not isinstance(criterion, Criterion):
if is_classification:
criterion = CRITERIA_CLF[self.criterion](
self.n_outputs_, self.n_classes_
)
else:
criterion = CRITERIA_REG[self.criterion](self.n_outputs_, n_samples)
else:
# Make a deepcopy in case the criterion has mutable attributes that
# might be shared and modified concurrently during parallel fitting
criterion = copy.deepcopy(criterion)
SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS
splitter = self.splitter
if self.monotonic_cst is None:
monotonic_cst = None
else:
if self.n_outputs_ > 1:
raise ValueError(
"Monotonicity constraints are not supported with multiple outputs."
)
# Check to correct monotonicity constraint' specification,
# by applying element-wise logical conjunction
# Note: we do not cast `np.asarray(self.monotonic_cst, dtype=np.int8)`
# straight away here so as to generate error messages for invalid
# values using the original values prior to any dtype related conversion.
monotonic_cst = np.asarray(self.monotonic_cst)
if monotonic_cst.shape[0] != X.shape[1]:
raise ValueError(
"monotonic_cst has shape {} but the input data "
"X has {} features.".format(monotonic_cst.shape[0], X.shape[1])
)
valid_constraints = np.isin(monotonic_cst, (-1, 0, 1))
if not np.all(valid_constraints):
unique_constaints_value = np.unique(monotonic_cst)
raise ValueError(
"monotonic_cst must be None or an array-like of -1, 0 or 1, but"
f" got {unique_constaints_value}"
)
monotonic_cst = np.asarray(monotonic_cst, dtype=np.int8)
if is_classifier(self):
if self.n_classes_[0] > 2:
raise ValueError(
"Monotonicity constraints are not supported with multiclass "
"classification"
)
# Binary classification trees are built by constraining probabilities
# of the *negative class* in order to make the implementation similar
# to regression trees.
# Since self.monotonic_cst encodes constraints on probabilities of the
# *positive class*, all signs must be flipped.
monotonic_cst *= -1
if not isinstance(self.splitter, Splitter):
splitter = SPLITTERS[self.splitter](
criterion,
self.max_features_,
min_samples_leaf,
min_weight_leaf,
random_state,
monotonic_cst,
)
if is_classifier(self):
self.tree_ = Tree(self.n_features_in_, self.n_classes_, self.n_outputs_)
else:
self.tree_ = Tree(
self.n_features_in_,
# TODO: tree shouldn't need this in this case
np.array([1] * self.n_outputs_, dtype=np.intp),
self.n_outputs_,
)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if max_leaf_nodes < 0:
builder = DepthFirstTreeBuilder(
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16,866 | imodels.tree.cart_ccp | _get_alpha | null | def _get_alpha(self, X, y, sample_weight=None, *args, **kwargs):
path = self.estimator_.cost_complexity_pruning_path(X, y)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
complexities = {}
low = 0
high = len(ccp_alphas) - 1
cur = 0
while low <= high:
cur = (high + low) // 2
est_params = self.estimator_.get_params()
est_params['ccp_alpha'] = ccp_alphas[cur]
copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
copied_estimator.fit(X, y)
if self._get_complexity(copied_estimator, self.complexity_measure) < self.desired_complexity:
high = cur - 1
elif self._get_complexity(copied_estimator, self.complexity_measure) > self.desired_complexity:
low = cur + 1
else:
break
self.alpha = ccp_alphas[cur]
# for alpha in ccp_alphas:
# est_params = self.estimator_.get_params()
# est_params['ccp_alpha'] = alpha
# copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
# copied_estimator.fit(X, y)
# complexities[alpha] = self._get_complexity(copied_estimator,self.complexity_measure)
# closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
# self.alpha = closest_alpha
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16,867 | imodels.tree.cart_ccp | _get_complexity | null | def _get_complexity(self, BaseEstimator, complexity_measure):
return compute_tree_complexity(BaseEstimator.tree_, complexity_measure)
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16,871 | sklearn.tree._classes | _more_tags | null | def _more_tags(self):
# XXX: nan is only support for dense arrays, but we set this for common test to
# pass, specifically: check_estimators_nan_inf
allow_nan = self.splitter == "best" and self.criterion in {
"gini",
"log_loss",
"entropy",
}
return {"multilabel": True, "allow_nan": allow_nan}
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16,872 | sklearn.tree._classes | _prune_tree | Prune tree using Minimal Cost-Complexity Pruning. | def _prune_tree(self):
"""Prune tree using Minimal Cost-Complexity Pruning."""
check_is_fitted(self)
if self.ccp_alpha == 0.0:
return
# build pruned tree
if is_classifier(self):
n_classes = np.atleast_1d(self.n_classes_)
pruned_tree = Tree(self.n_features_in_, n_classes, self.n_outputs_)
else:
pruned_tree = Tree(
self.n_features_in_,
# TODO: the tree shouldn't need this param
np.array([1] * self.n_outputs_, dtype=np.intp),
self.n_outputs_,
)
_build_pruned_tree_ccp(pruned_tree, self.tree_, self.ccp_alpha)
self.tree_ = pruned_tree
| (self) | [
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0.008456484414637089,
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16,875 | sklearn.tree._classes | _support_missing_values | null | def _support_missing_values(self, X):
return (
not issparse(X)
and self._get_tags()["allow_nan"]
and self.monotonic_cst is None
)
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16,876 | sklearn.tree._classes | _validate_X_predict | Validate the training data on predict (probabilities). | def _validate_X_predict(self, X, check_input):
"""Validate the training data on predict (probabilities)."""
if check_input:
if self._support_missing_values(X):
force_all_finite = "allow-nan"
else:
force_all_finite = True
X = self._validate_data(
X,
dtype=DTYPE,
accept_sparse="csr",
reset=False,
force_all_finite=force_all_finite,
)
if issparse(X) and (
X.indices.dtype != np.intc or X.indptr.dtype != np.intc
):
raise ValueError("No support for np.int64 index based sparse matrices")
else:
# The number of features is checked regardless of `check_input`
self._check_n_features(X, reset=False)
return X
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16,879 | sklearn.tree._classes | apply | Return the index of the leaf that each sample is predicted as.
.. versionadded:: 0.17
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
X_leaves : array-like of shape (n_samples,)
For each datapoint x in X, return the index of the leaf x
ends up in. Leaves are numbered within
``[0; self.tree_.node_count)``, possibly with gaps in the
numbering.
| def apply(self, X, check_input=True):
"""Return the index of the leaf that each sample is predicted as.
.. versionadded:: 0.17
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
X_leaves : array-like of shape (n_samples,)
For each datapoint x in X, return the index of the leaf x
ends up in. Leaves are numbered within
``[0; self.tree_.node_count)``, possibly with gaps in the
numbering.
"""
check_is_fitted(self)
X = self._validate_X_predict(X, check_input)
return self.tree_.apply(X)
| (self, X, check_input=True) | [
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16,880 | sklearn.tree._classes | cost_complexity_pruning_path | Compute the pruning path during Minimal Cost-Complexity Pruning.
See :ref:`minimal_cost_complexity_pruning` for details on the pruning
process.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
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. Splits are also
ignored if they would result in any single class carrying a
negative weight in either child node.
Returns
-------
ccp_path : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
ccp_alphas : ndarray
Effective alphas of subtree during pruning.
impurities : ndarray
Sum of the impurities of the subtree leaves for the
corresponding alpha value in ``ccp_alphas``.
| def cost_complexity_pruning_path(self, X, y, sample_weight=None):
"""Compute the pruning path during Minimal Cost-Complexity Pruning.
See :ref:`minimal_cost_complexity_pruning` for details on the pruning
process.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
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. Splits are also
ignored if they would result in any single class carrying a
negative weight in either child node.
Returns
-------
ccp_path : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
ccp_alphas : ndarray
Effective alphas of subtree during pruning.
impurities : ndarray
Sum of the impurities of the subtree leaves for the
corresponding alpha value in ``ccp_alphas``.
"""
est = clone(self).set_params(ccp_alpha=0.0)
est.fit(X, y, sample_weight=sample_weight)
return Bunch(**ccp_pruning_path(est.tree_))
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16,881 | sklearn.tree._classes | decision_path | Return the decision path in the tree.
.. versionadded:: 0.18
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
indicator : sparse matrix of shape (n_samples, n_nodes)
Return a node indicator CSR matrix where non zero elements
indicates that the samples goes through the nodes.
| def decision_path(self, X, check_input=True):
"""Return the decision path in the tree.
.. versionadded:: 0.18
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
indicator : sparse matrix of shape (n_samples, n_nodes)
Return a node indicator CSR matrix where non zero elements
indicates that the samples goes through the nodes.
"""
X = self._validate_X_predict(X, check_input)
return self.tree_.decision_path(X)
| (self, X, check_input=True) | [
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16,882 | imodels.tree.cart_ccp | fit | null | def fit(self, X, y, sample_weight=None, *args, **kwargs):
params_for_fitting = self.estimator_.get_params()
self._get_alpha(X, y, sample_weight, *args, **kwargs)
params_for_fitting['ccp_alpha'] = self.alpha
self.estimator_.set_params(**params_for_fitting)
self.estimator_.fit(X, y, *args, **kwargs)
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16,883 | sklearn.tree._classes | get_depth | Return the depth of the decision tree.
The depth of a tree is the maximum distance between the root
and any leaf.
Returns
-------
self.tree_.max_depth : int
The maximum depth of the tree.
| def get_depth(self):
"""Return the depth of the decision tree.
The depth of a tree is the maximum distance between the root
and any leaf.
Returns
-------
self.tree_.max_depth : int
The maximum depth of the tree.
"""
check_is_fitted(self)
return self.tree_.max_depth
| (self) | [
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] |
16,885 | sklearn.tree._classes | get_n_leaves | Return the number of leaves of the decision tree.
Returns
-------
self.tree_.n_leaves : int
Number of leaves.
| def get_n_leaves(self):
"""Return the number of leaves of the decision tree.
Returns
-------
self.tree_.n_leaves : int
Number of leaves.
"""
check_is_fitted(self)
return self.tree_.n_leaves
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16,887 | imodels.tree.cart_ccp | predict | null | def predict(self, X, *args, **kwargs):
return self.estimator_.predict(X, *args, **kwargs)
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16,888 | sklearn.tree._classes | predict_log_proba | Predict class log-probabilities of the input samples X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
| def predict_log_proba(self, X):
"""Predict class log-probabilities of the input samples X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \
such arrays if n_outputs > 1
The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return np.log(proba)
else:
for k in range(self.n_outputs_):
proba[k] = np.log(proba[k])
return proba
| (self, X) | [
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16,889 | imodels.tree.cart_ccp | predict_proba | null | def predict_proba(self, *args, **kwargs):
if hasattr(self.estimator_, 'predict_proba'):
return self.estimator_.predict_proba(*args, **kwargs)
else:
return NotImplemented
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16,890 | imodels.tree.cart_ccp | score | null | def score(self, *args, **kwargs):
if hasattr(self.estimator_, 'score'):
return self.estimator_.score(*args, **kwargs)
else:
return NotImplemented
| (self, *args, **kwargs) | [
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16,893 | 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
----------
check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``check_input`` 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.tree.cart_ccp.DecisionTreeCCPClassifier, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.tree.cart_ccp.DecisionTreeCCPClassifier | [
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16,894 | 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
----------
check_input : str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for ``check_input`` 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.tree.cart_ccp.DecisionTreeCCPClassifier, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$') -> imodels.tree.cart_ccp.DecisionTreeCCPClassifier | [
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16,896 | imodels.tree.cart_ccp | DecisionTreeCCPRegressor | null | class DecisionTreeCCPRegressor(BaseEstimator):
def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args,
**kwargs):
self.desired_complexity = desired_complexity
# print('est', estimator_)
self.estimator_ = estimator_
self.alpha = 0.0
self.complexity_measure = complexity_measure
def _get_alpha(self, X, y, sample_weight=None):
path = self.estimator_.cost_complexity_pruning_path(X, y)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
complexities = {}
low = 0
high = len(ccp_alphas) - 1
cur = 0
while low <= high:
cur = (high + low) // 2
est_params = self.estimator_.get_params()
est_params['ccp_alpha'] = ccp_alphas[cur]
copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
copied_estimator.fit(X, y)
if self._get_complexity(copied_estimator, self.complexity_measure) < self.desired_complexity:
high = cur - 1
elif self._get_complexity(copied_estimator, self.complexity_measure) > self.desired_complexity:
low = cur + 1
else:
break
self.alpha = ccp_alphas[cur]
# path = self.estimator_.cost_complexity_pruning_path(X,y)
# ccp_alphas, impurities = path.ccp_alphas, path.impurities
# complexities = {}
# for alpha in ccp_alphas:
# est_params = self.estimator_.get_params()
# est_params['ccp_alpha'] = alpha
# copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
# copied_estimator.fit(X, y)
# complexities[alpha] = self._get_complexity(copied_estimator,self.complexity_measure)
# closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
# self.alpha = closest_alpha
def fit(self, X, y, sample_weight=None):
params_for_fitting = self.estimator_.get_params()
self._get_alpha(X, y, sample_weight)
params_for_fitting['ccp_alpha'] = self.alpha
self.estimator_.set_params(**params_for_fitting)
self.estimator_.fit(X, y)
def _get_complexity(self, BaseEstimator, complexity_measure):
return compute_tree_complexity(BaseEstimator.tree_, self.complexity_measure)
def predict(self, X, *args, **kwargs):
return self.estimator_.predict(X, *args, **kwargs)
def score(self, *args, **kwargs):
if hasattr(self.estimator_, 'score'):
return self.estimator_.score(*args, **kwargs)
else:
return NotImplemented
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16,898 | imodels.tree.cart_ccp | __init__ | null | def __init__(self, estimator_: BaseEstimator, desired_complexity: int = 1, complexity_measure='max_rules', *args,
**kwargs):
self.desired_complexity = desired_complexity
# print('est', estimator_)
self.estimator_ = estimator_
self.alpha = 0.0
self.complexity_measure = complexity_measure
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16,904 | imodels.tree.cart_ccp | _get_alpha | null | def _get_alpha(self, X, y, sample_weight=None):
path = self.estimator_.cost_complexity_pruning_path(X, y)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
complexities = {}
low = 0
high = len(ccp_alphas) - 1
cur = 0
while low <= high:
cur = (high + low) // 2
est_params = self.estimator_.get_params()
est_params['ccp_alpha'] = ccp_alphas[cur]
copied_estimator = deepcopy(self.estimator_).set_params(**est_params)
copied_estimator.fit(X, y)
if self._get_complexity(copied_estimator, self.complexity_measure) < self.desired_complexity:
high = cur - 1
elif self._get_complexity(copied_estimator, self.complexity_measure) > self.desired_complexity:
low = cur + 1
else:
break
self.alpha = ccp_alphas[cur]
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16,905 | imodels.tree.cart_ccp | _get_complexity | null | def _get_complexity(self, BaseEstimator, complexity_measure):
return compute_tree_complexity(BaseEstimator.tree_, self.complexity_measure)
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16,914 | imodels.tree.cart_ccp | fit | null | # closest_alpha, closest_leaves = min(complexities.items(), key=lambda x: abs(self.desired_complexity - x[1]))
# self.alpha = closest_alpha
def fit(self, X, y, sample_weight=None):
params_for_fitting = self.estimator_.get_params()
self._get_alpha(X, y, sample_weight)
params_for_fitting['ccp_alpha'] = self.alpha
self.estimator_.set_params(**params_for_fitting)
self.estimator_.fit(X, y)
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16,921 | sklearn.tree._classes | DecisionTreeClassifier | A decision tree classifier.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : {"gini", "entropy", "log_loss"}, default="gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "log_loss" and "entropy" both for the
Shannon information gain, see :ref:`tree_mathematical_formulation`.
splitter : {"best", "random"}, default="best"
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : int, float or {"sqrt", "log2"}, default=None
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at
each split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
random_state : int, RandomState instance or None, default=None
Controls the randomness of the estimator. The features are always
randomly permuted at each split, even if ``splitter`` is set to
``"best"``. When ``max_features < n_features``, the algorithm will
select ``max_features`` at random at each split before finding the best
split among them. But the best found split may vary across different
runs, even if ``max_features=n_features``. That is the case, if the
improvement of the criterion is identical for several splits and one
split has to be selected at random. To obtain a deterministic behaviour
during fitting, ``random_state`` has to be fixed to an integer.
See :term:`Glossary <random_state>` for details.
max_leaf_nodes : int, default=None
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
class_weight : dict, list of dict or "balanced", default=None
Weights associated with classes in the form ``{class_label: weight}``.
If None, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}].
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
monotonic_cst : array-like of int of shape (n_features), default=None
Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonic increase
- 0: no constraint
- -1: monotonic decrease
If monotonic_cst is None, no constraints are applied.
Monotonicity constraints are not supported for:
- multiclass classifications (i.e. when `n_classes > 2`),
- multioutput classifications (i.e. when `n_outputs_ > 1`),
- classifications trained on data with missing values.
The constraints hold over the probability of the positive class.
Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
.. versionadded:: 1.4
Attributes
----------
classes_ : ndarray of shape (n_classes,) or list of ndarray
The classes labels (single output problem),
or a list of arrays of class labels (multi-output problem).
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance [4]_.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
max_features_ : int
The inferred value of max_features.
n_classes_ : int or list of int
The number of classes (for single output problems),
or a list containing the number of classes for each
output (for multi-output problems).
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
n_outputs_ : int
The number of outputs when ``fit`` is performed.
tree_ : Tree instance
The underlying Tree object. Please refer to
``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
:ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
for basic usage of these attributes.
See Also
--------
DecisionTreeRegressor : A decision tree regressor.
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
The :meth:`predict` method operates using the :func:`numpy.argmax`
function on the outputs of :meth:`predict_proba`. This means that in
case the highest predicted probabilities are tied, the classifier will
predict the tied class with the lowest index in :term:`classes_`.
References
----------
.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
and Regression Trees", Wadsworth, Belmont, CA, 1984.
.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
Learning", Springer, 2009.
.. [4] L. Breiman, and A. Cutler, "Random Forests",
https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier(random_state=0)
>>> iris = load_iris()
>>> cross_val_score(clf, iris.data, iris.target, cv=10)
... # doctest: +SKIP
...
array([ 1. , 0.93..., 0.86..., 0.93..., 0.93...,
0.93..., 0.93..., 1. , 0.93..., 1. ])
| class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
"""A decision tree classifier.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : {"gini", "entropy", "log_loss"}, default="gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "log_loss" and "entropy" both for the
Shannon information gain, see :ref:`tree_mathematical_formulation`.
splitter : {"best", "random"}, default="best"
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : int, float or {"sqrt", "log2"}, default=None
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at
each split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
random_state : int, RandomState instance or None, default=None
Controls the randomness of the estimator. The features are always
randomly permuted at each split, even if ``splitter`` is set to
``"best"``. When ``max_features < n_features``, the algorithm will
select ``max_features`` at random at each split before finding the best
split among them. But the best found split may vary across different
runs, even if ``max_features=n_features``. That is the case, if the
improvement of the criterion is identical for several splits and one
split has to be selected at random. To obtain a deterministic behaviour
during fitting, ``random_state`` has to be fixed to an integer.
See :term:`Glossary <random_state>` for details.
max_leaf_nodes : int, default=None
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
class_weight : dict, list of dict or "balanced", default=None
Weights associated with classes in the form ``{class_label: weight}``.
If None, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}].
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
monotonic_cst : array-like of int of shape (n_features), default=None
Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonic increase
- 0: no constraint
- -1: monotonic decrease
If monotonic_cst is None, no constraints are applied.
Monotonicity constraints are not supported for:
- multiclass classifications (i.e. when `n_classes > 2`),
- multioutput classifications (i.e. when `n_outputs_ > 1`),
- classifications trained on data with missing values.
The constraints hold over the probability of the positive class.
Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
.. versionadded:: 1.4
Attributes
----------
classes_ : ndarray of shape (n_classes,) or list of ndarray
The classes labels (single output problem),
or a list of arrays of class labels (multi-output problem).
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance [4]_.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
max_features_ : int
The inferred value of max_features.
n_classes_ : int or list of int
The number of classes (for single output problems),
or a list containing the number of classes for each
output (for multi-output problems).
n_features_in_ : int
Number of features seen duri | (*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, monotonic_cst=None) | [
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16,923 | sklearn.tree._classes | __init__ | null | def __init__(
self,
*,
criterion="gini",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=None,
random_state=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
class_weight=None,
ccp_alpha=0.0,
monotonic_cst=None,
):
super().__init__(
criterion=criterion,
splitter=splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
class_weight=class_weight,
random_state=random_state,
min_impurity_decrease=min_impurity_decrease,
monotonic_cst=monotonic_cst,
ccp_alpha=ccp_alpha,
)
| (self, *, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, monotonic_cst=None) | [
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16,945 | sklearn.tree._classes | fit | Build a decision tree classifier from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
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. Splits are also
ignored if they would result in any single class carrying a
negative weight in either child node.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
self : DecisionTreeClassifier
Fitted estimator.
| def _more_tags(self):
# XXX: nan is only support for dense arrays, but we set this for common test to
# pass, specifically: check_estimators_nan_inf
allow_nan = self.splitter == "best" and self.criterion in {
"squared_error",
"friedman_mse",
"poisson",
}
return {"allow_nan": allow_nan}
| (self, X, y, sample_weight=None, check_input=True) | [
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16,950 | sklearn.tree._classes | predict | Predict class or regression value for X.
For a classification model, the predicted class for each sample in X is
returned. For a regression model, the predicted value based on X is
returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes, or the predict values.
| def predict(self, X, check_input=True):
"""Predict class or regression value for X.
For a classification model, the predicted class for each sample in X is
returned. For a regression model, the predicted value based on X is
returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes, or the predict values.
"""
check_is_fitted(self)
X = self._validate_X_predict(X, check_input)
proba = self.tree_.predict(X)
n_samples = X.shape[0]
# Classification
if is_classifier(self):
if self.n_outputs_ == 1:
return self.classes_.take(np.argmax(proba, axis=1), axis=0)
else:
class_type = self.classes_[0].dtype
predictions = np.zeros((n_samples, self.n_outputs_), dtype=class_type)
for k in range(self.n_outputs_):
predictions[:, k] = self.classes_[k].take(
np.argmax(proba[:, k], axis=1), axis=0
)
return predictions
# Regression
else:
if self.n_outputs_ == 1:
return proba[:, 0]
else:
return proba[:, :, 0]
| (self, X, check_input=True) | [
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16,952 | sklearn.tree._classes | predict_proba | Predict class probabilities of the input samples X.
The predicted class probability is the fraction of samples of the same
class in a leaf.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
| def predict_proba(self, X, check_input=True):
"""Predict class probabilities of the input samples X.
The predicted class probability is the fraction of samples of the same
class in a leaf.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \
such arrays if n_outputs > 1
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
check_is_fitted(self)
X = self._validate_X_predict(X, check_input)
proba = self.tree_.predict(X)
if self.n_outputs_ == 1:
return proba[:, : self.n_classes_]
else:
all_proba = []
for k in range(self.n_outputs_):
proba_k = proba[:, k, : self.n_classes_[k]]
all_proba.append(proba_k)
return all_proba
| (self, X, check_input=True) | [
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16,959 | sklearn.tree._classes | DecisionTreeRegressor | A decision tree regressor.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : {"squared_error", "friedman_mse", "absolute_error", "poisson"}, default="squared_error"
The function to measure the quality of a split. Supported criteria
are "squared_error" for the mean squared error, which is equal to
variance reduction as feature selection criterion and minimizes the L2
loss using the mean of each terminal node, "friedman_mse", which uses
mean squared error with Friedman's improvement score for potential
splits, "absolute_error" for the mean absolute error, which minimizes
the L1 loss using the median of each terminal node, and "poisson" which
uses reduction in Poisson deviance to find splits.
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
.. versionadded:: 0.24
Poisson deviance criterion.
splitter : {"best", "random"}, default="best"
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : int, float or {"sqrt", "log2"}, default=None
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at each
split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
random_state : int, RandomState instance or None, default=None
Controls the randomness of the estimator. The features are always
randomly permuted at each split, even if ``splitter`` is set to
``"best"``. When ``max_features < n_features``, the algorithm will
select ``max_features`` at random at each split before finding the best
split among them. But the best found split may vary across different
runs, even if ``max_features=n_features``. That is the case, if the
improvement of the criterion is identical for several splits and one
split has to be selected at random. To obtain a deterministic behaviour
during fitting, ``random_state`` has to be fixed to an integer.
See :term:`Glossary <random_state>` for details.
max_leaf_nodes : int, default=None
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
monotonic_cst : array-like of int of shape (n_features), default=None
Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonic increase
- 0: no constraint
- -1: monotonic decrease
If monotonic_cst is None, no constraints are applied.
Monotonicity constraints are not supported for:
- multioutput regressions (i.e. when `n_outputs_ > 1`),
- regressions trained on data with missing values.
Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
.. versionadded:: 1.4
Attributes
----------
feature_importances_ : ndarray of shape (n_features,)
The feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the
(normalized) total reduction of the criterion brought
by that feature. It is also known as the Gini importance [4]_.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
max_features_ : int
The inferred value of max_features.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
n_outputs_ : int
The number of outputs when ``fit`` is performed.
tree_ : Tree instance
The underlying Tree object. Please refer to
``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
:ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
for basic usage of these attributes.
See Also
--------
DecisionTreeClassifier : A decision tree classifier.
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
References
----------
.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
and Regression Trees", Wadsworth, Belmont, CA, 1984.
.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
Learning", Springer, 2009.
.. [4] L. Breiman, and A. Cutler, "Random Forests",
https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Examples
--------
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = DecisionTreeRegressor(random_state=0)
>>> cross_val_score(regressor, X, y, cv=10)
... # doctest: +SKIP
...
array([-0.39..., -0.46..., 0.02..., 0.06..., -0.50...,
0.16..., 0.11..., -0.73..., -0.30..., -0.00...])
| class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
"""A decision tree regressor.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : {"squared_error", "friedman_mse", "absolute_error", \
"poisson"}, default="squared_error"
The function to measure the quality of a split. Supported criteria
are "squared_error" for the mean squared error, which is equal to
variance reduction as feature selection criterion and minimizes the L2
loss using the mean of each terminal node, "friedman_mse", which uses
mean squared error with Friedman's improvement score for potential
splits, "absolute_error" for the mean absolute error, which minimizes
the L1 loss using the median of each terminal node, and "poisson" which
uses reduction in Poisson deviance to find splits.
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
.. versionadded:: 0.24
Poisson deviance criterion.
splitter : {"best", "random"}, default="best"
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : int, float or {"sqrt", "log2"}, default=None
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`max(1, int(max_features * n_features_in_))` features are considered at each
split.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
random_state : int, RandomState instance or None, default=None
Controls the randomness of the estimator. The features are always
randomly permuted at each split, even if ``splitter`` is set to
``"best"``. When ``max_features < n_features``, the algorithm will
select ``max_features`` at random at each split before finding the best
split among them. But the best found split may vary across different
runs, even if ``max_features=n_features``. That is the case, if the
improvement of the criterion is identical for several splits and one
split has to be selected at random. To obtain a deterministic behaviour
during fitting, ``random_state`` has to be fixed to an integer.
See :term:`Glossary <random_state>` for details.
max_leaf_nodes : int, default=None
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
monotonic_cst : array-like of int of shape (n_features), default=None
Indicates the monotonicity constraint to enforce on each feature.
- 1: monotonic increase
- 0: no constraint
- -1: monotonic decrease
If monotonic_cst is None, no constraints are applied.
Monotonicity constraints are not supported for:
- multioutput regressions (i.e. when `n_outputs_ > 1`),
- regressions trained on data with missing values.
Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
.. versionadded:: 1.4
Attributes
----------
feature_importances_ : ndarray of shape (n_features,)
The feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the
(normalized) total reduction of the criterion brought
by that feature. It is also known as the Gini importance [4]_.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
max_features_ : int
The inferred value of max_features.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
n_outputs_ : int
The number of outputs when ``fit`` is performed.
tree_ : Tree instance
The underlying Tree object. Please refer to
``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
:ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
for basic usage of these attributes.
See Also
--------
DecisionTreeClassifier : A decision tree classifier.
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
References
- | (*, criterion='squared_error', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, ccp_alpha=0.0, monotonic_cst=None) | [
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16,961 | sklearn.tree._classes | __init__ | null | def __init__(
self,
*,
criterion="squared_error",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=None,
random_state=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
ccp_alpha=0.0,
monotonic_cst=None,
):
super().__init__(
criterion=criterion,
splitter=splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
random_state=random_state,
min_impurity_decrease=min_impurity_decrease,
ccp_alpha=ccp_alpha,
monotonic_cst=monotonic_cst,
)
| (self, *, criterion='squared_error', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, ccp_alpha=0.0, monotonic_cst=None) | [
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16,968 | sklearn.tree._classes | _compute_partial_dependence_recursion | Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features)
The grid points on which the partial dependence should be
evaluated.
target_features : ndarray of shape (n_target_features)
The set of target features for which the partial dependence
should be evaluated.
Returns
-------
averaged_predictions : ndarray of shape (n_samples,)
The value of the partial dependence function on each grid point.
| def _compute_partial_dependence_recursion(self, grid, target_features):
"""Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features)
The grid points on which the partial dependence should be
evaluated.
target_features : ndarray of shape (n_target_features)
The set of target features for which the partial dependence
should be evaluated.
Returns
-------
averaged_predictions : ndarray of shape (n_samples,)
The value of the partial dependence function on each grid point.
"""
grid = np.asarray(grid, dtype=DTYPE, order="C")
averaged_predictions = np.zeros(
shape=grid.shape[0], dtype=np.float64, order="C"
)
self.tree_.compute_partial_dependence(
grid, target_features, averaged_predictions
)
return averaged_predictions
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16,973 | sklearn.tree._classes | _more_tags | null | def _more_tags(self):
# XXX: nan is only support for dense arrays, but we set this for common test to
# pass, specifically: check_estimators_nan_inf
allow_nan = self.splitter == "best" and self.criterion in {
"squared_error",
"friedman_mse",
"poisson",
}
return {"allow_nan": allow_nan}
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16,984 | sklearn.tree._classes | fit | Build a decision tree regressor from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use ``dtype=np.float64`` and
``order='C'`` for maximum efficiency.
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.
check_input : bool, default=True
Allow to bypass several input checking.
Don't use this parameter unless you know what you're doing.
Returns
-------
self : DecisionTreeRegressor
Fitted estimator.
| def _more_tags(self):
# XXX: nan is only support for dense arrays, but we set this for common test to
# pass, specifically: check_estimators_nan_inf
allow_nan = self.splitter == "best" and self.criterion in {
"squared_error",
"friedman_mse",
"poisson",
}
return {"allow_nan": allow_nan}
| (self, X, y, sample_weight=None, check_input=True) | [
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] |
16,995 | imodels.util.distillation | DistilledRegressor |
Class to implement distillation. Currently only supports regression.
Params
------
teacher: initial model to be trained
must be a regressor or a binary classifier
student: model to be distilled from teacher's predictions
must be a regressor
| class DistilledRegressor(BaseEstimator, RegressorMixin):
"""
Class to implement distillation. Currently only supports regression.
Params
------
teacher: initial model to be trained
must be a regressor or a binary classifier
student: model to be distilled from teacher's predictions
must be a regressor
"""
def __init__(self, teacher: BaseEstimator, student: BaseEstimator,
n_iters_teacher: int=1):
self.teacher = teacher
self.student = student
self.n_iters_teacher = n_iters_teacher
self._validate_student()
self._check_teacher_type()
def _validate_student(self):
if is_regressor(self.student):
pass
else:
if not hasattr(self.student, "prediction_task"):
raise ValueError("Student must be either a scikit-learn or imodels regressor")
elif self.student.prediction_task == "classification":
raise ValueError("Student must be a regressor")
def _check_teacher_type(self):
if hasattr(self.teacher, "prediction_task"):
self.teacher_type = self.teacher.prediction_task
elif hasattr(self.teacher, "_estimator_type"):
if is_regressor(self.teacher):
self.teacher_type = "regression"
else:
self.teacher_type = "classification"
def set_teacher_params(self, **params):
self.teacher.set_params(**params)
def set_student_params(self, **params):
self.student.set_params(**params)
def fit(self, X, y, **kwargs):
# fit teacher
for iter_teacher in range(self.n_iters_teacher):
self.teacher.fit(X, y, **kwargs)
if self.teacher_type == "regression":
y = self.teacher.predict(X)
else:
y = self.teacher.predict_proba(X)[:, 1] # assumes binary classifier
# fit student
self.student.fit(X, y)
def predict(self, X):
return self.student.predict(X)
| (teacher: sklearn.base.BaseEstimator, student: sklearn.base.BaseEstimator, n_iters_teacher: int = 1) | [
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16,997 | imodels.util.distillation | __init__ | null | def __init__(self, teacher: BaseEstimator, student: BaseEstimator,
n_iters_teacher: int=1):
self.teacher = teacher
self.student = student
self.n_iters_teacher = n_iters_teacher
self._validate_student()
self._check_teacher_type()
| (self, teacher: sklearn.base.BaseEstimator, student: sklearn.base.BaseEstimator, n_iters_teacher: int = 1) | [
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17,003 | imodels.util.distillation | _check_teacher_type | null | def _check_teacher_type(self):
if hasattr(self.teacher, "prediction_task"):
self.teacher_type = self.teacher.prediction_task
elif hasattr(self.teacher, "_estimator_type"):
if is_regressor(self.teacher):
self.teacher_type = "regression"
else:
self.teacher_type = "classification"
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17,012 | imodels.util.distillation | _validate_student | null | def _validate_student(self):
if is_regressor(self.student):
pass
else:
if not hasattr(self.student, "prediction_task"):
raise ValueError("Student must be either a scikit-learn or imodels regressor")
elif self.student.prediction_task == "classification":
raise ValueError("Student must be a regressor")
| (self) | [
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17,013 | imodels.util.distillation | fit | null | def fit(self, X, y, **kwargs):
# fit teacher
for iter_teacher in range(self.n_iters_teacher):
self.teacher.fit(X, y, **kwargs)
if self.teacher_type == "regression":
y = self.teacher.predict(X)
else:
y = self.teacher.predict_proba(X)[:, 1] # assumes binary classifier
# fit student
self.student.fit(X, y)
| (self, X, y, **kwargs) | [
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17,016 | imodels.util.distillation | predict | null | def predict(self, X):
return self.student.predict(X)
| (self, X) | [
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17,020 | imodels.util.distillation | set_student_params | null | def set_student_params(self, **params):
self.student.set_params(**params)
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17,021 | imodels.util.distillation | set_teacher_params | null | def set_teacher_params(self, **params):
self.teacher.set_params(**params)
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17,022 | imodels.tree.figs | FIGSClassifier | null | class FIGSClassifier(FIGS, ClassifierMixin):
...
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17,024 | imodels.tree.figs | __init__ |
Params
------
max_rules: int
Max total number of rules across all trees
max_trees: int
Max total number of trees
min_impurity_decrease: float
A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
max_features
The number of features to consider when looking for the best split (see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
| def __init__(
self,
max_rules: int = 12,
max_trees: int = None,
min_impurity_decrease: float = 0.0,
random_state=None,
max_features: str = None,
):
"""
Params
------
max_rules: int
Max total number of rules across all trees
max_trees: int
Max total number of trees
min_impurity_decrease: float
A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
max_features
The number of features to consider when looking for the best split (see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
"""
super().__init__()
self.max_rules = max_rules
self.max_trees = max_trees
self.min_impurity_decrease = min_impurity_decrease
self.random_state = random_state
self.max_features = max_features
self._init_decision_function()
| (self, max_rules: int = 12, max_trees: Optional[int] = None, min_impurity_decrease: float = 0.0, random_state=None, max_features: Optional[str] = None) | [
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17,028 | imodels.tree.figs | __str__ | null | def __str__(self):
if not hasattr(self, "trees_"):
s = self.__class__.__name__
s += "("
s += "max_rules="
s += repr(self.max_rules)
s += ")"
return s
else:
s = "> ------------------------------\n"
s += "> FIGS-Fast Interpretable Greedy-Tree Sums:\n"
s += '> \tPredictions are made by summing the "Val" reached by traversing each tree.\n'
s += "> \tFor classifiers, a sigmoid function is then applied to the sum.\n"
s += "> ------------------------------\n"
s += "\n\t+\n".join([self._tree_to_str(t) for t in self.trees_])
if hasattr(self, "feature_names_") and self.feature_names_ is not None:
for i in range(len(self.feature_names_))[::-1]:
s = s.replace(f"X_{i}", self.feature_names_[i])
return s
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] |
17,031 | imodels.tree.figs | _construct_node_with_stump |
Params
------
compare_nodes_with_sample_weight: Deprecated
If this is set to true and sample_weight is passed, use sample_weight to compare nodes
Otherwise, use sample_weight only for picking a split given a particular node
| def _construct_node_with_stump(
self,
X,
y,
idxs,
tree_num,
sample_weight=None,
compare_nodes_with_sample_weight=True,
max_features=None,
):
"""
Params
------
compare_nodes_with_sample_weight: Deprecated
If this is set to true and sample_weight is passed, use sample_weight to compare nodes
Otherwise, use sample_weight only for picking a split given a particular node
"""
# array indices
SPLIT = 0
LEFT = 1
RIGHT = 2
# fit stump
stump = tree.DecisionTreeRegressor(max_depth=1, max_features=max_features)
sweight = None
if sample_weight is not None:
sweight = sample_weight[idxs]
stump.fit(X[idxs], y[idxs], sample_weight=sweight)
# these are all arrays, arr[0] is split node
# note: -2 is dummy
feature = stump.tree_.feature
threshold = stump.tree_.threshold
impurity = stump.tree_.impurity
n_node_samples = stump.tree_.n_node_samples
value = stump.tree_.value
# no split
if len(feature) == 1:
# print('no split found!', idxs.sum(), impurity, feature)
return Node(
idxs=idxs,
value=value[SPLIT],
tree_num=tree_num,
feature=feature[SPLIT],
threshold=threshold[SPLIT],
impurity=impurity[SPLIT],
impurity_reduction=None,
)
# manage sample weights
idxs_split = X[:, feature[SPLIT]] <= threshold[SPLIT]
idxs_left = idxs_split & idxs
idxs_right = ~idxs_split & idxs
if sample_weight is None:
n_node_samples_left = n_node_samples[LEFT]
n_node_samples_right = n_node_samples[RIGHT]
else:
n_node_samples_left = sample_weight[idxs_left].sum()
n_node_samples_right = sample_weight[idxs_right].sum()
n_node_samples_split = n_node_samples_left + n_node_samples_right
# calculate impurity
impurity_reduction = (
impurity[SPLIT]
- impurity[LEFT] * n_node_samples_left / n_node_samples_split
- impurity[RIGHT] * n_node_samples_right / n_node_samples_split
) * n_node_samples_split
node_split = Node(
idxs=idxs,
value=value[SPLIT],
tree_num=tree_num,
feature=feature[SPLIT],
threshold=threshold[SPLIT],
impurity=impurity[SPLIT],
impurity_reduction=impurity_reduction,
)
# print('\t>>>', node_split, 'impurity', impurity, 'num_pts', idxs.sum(), 'imp_reduc', impurity_reduction)
# manage children
node_left = Node(
idxs=idxs_left,
value=value[LEFT],
impurity=impurity[LEFT],
tree_num=tree_num,
)
node_right = Node(
idxs=idxs_right,
value=value[RIGHT],
impurity=impurity[RIGHT],
tree_num=tree_num,
)
node_split.setattrs(
left_temp=node_left,
right_temp=node_right,
)
return node_split
| (self, X, y, idxs, tree_num, sample_weight=None, compare_nodes_with_sample_weight=True, max_features=None) | [
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17,032 | imodels.tree.figs | _encode_categories | null | def _encode_categories(self, X, categorical_features):
encoder = None
if hasattr(self, "_encoder"):
encoder = self._encoder
return encode_categories(X, categorical_features, encoder)
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17,036 | imodels.tree.figs | _init_decision_function | Sets decision function based on _estimator_type | def _init_decision_function(self):
"""Sets decision function based on _estimator_type"""
# used by sklearn GridSearchCV, BaggingClassifier
if isinstance(self, ClassifierMixin):
def decision_function(x):
return self.predict_proba(x)[:, 1]
elif isinstance(self, RegressorMixin):
decision_function = self.predict
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