csinva
/
imodels
mirror of https://github.com/csinva/imodels


  
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<main>
<article id="content">
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from typing import List

import numpy as np
import pandas as pd

from imodels.rule_set.skope_rules import SkopeRulesClassifier
from imodels.util.convert import itemsets_to_rules
from imodels.util.extract import extract_fpgrowth
from imodels.util.rule import Rule
from imodels.util.score import score_precision_recall


class FPSkopeClassifier(SkopeRulesClassifier):

    def __init__(self,
                 minsupport=0.1,
                 maxcardinality=2,
                 verbose=False,
                 precision_min=0.5,
                 recall_min=0.01,
                 n_estimators=10,
                 max_samples=.8,
                 max_samples_features=1.,
                 bootstrap=False,
                 bootstrap_features=False,
                 max_depth=3,
                 max_depth_duplication=None,
                 max_features=1.,
                 min_samples_split=2,
                 n_jobs=1,
                 random_state=None):
        super().__init__(precision_min,
                         recall_min,
                         n_estimators,
                         max_samples,
                         max_samples_features,
                         bootstrap,
                         bootstrap_features,
                         max_depth,
                         max_depth_duplication,
                         max_features,
                         min_samples_split,
                         n_jobs,
                         random_state,
                         verbose)
        self.minsupport = minsupport
        self.maxcardinality = maxcardinality
        self.verbose = verbose

    def fit(self, X, y=None, feature_names=None, undiscretized_features=[], sample_weight=None):
        self.undiscretized_features = undiscretized_features
        super().fit(X, y, feature_names=feature_names, sample_weight=sample_weight)
        return self

    def _extract_rules(self, X, y) -&gt; List[str]:
        X = pd.DataFrame(X, columns=self.feature_placeholders)
        itemsets = extract_fpgrowth(X, minsupport=self.minsupport,
                                    maxcardinality=self.maxcardinality,
                                    verbose=self.verbose)
        return [itemsets_to_rules(itemsets)], [np.arange(X.shape[0])], [np.arange(len(self.feature_names))]

    def _score_rules(self, X, y, rules) -&gt; List[Rule]:
        return score_precision_recall(X, y,
                                      rules,
                                      self.estimators_samples_,
                                      self.estimators_features_,
                                      self.feature_placeholders,
                                      oob=False)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="imodels.rule_set.fpskope.FPSkopeClassifier"><code class="flex name class">
<span>class <span class="ident">FPSkopeClassifier</span></span>
<span>(</span><span>minsupport=0.1, maxcardinality=2, verbose=False, precision_min=0.5, recall_min=0.01, n_estimators=10, max_samples=0.8, max_samples_features=1.0, bootstrap=False, bootstrap_features=False, max_depth=3, max_depth_duplication=None, max_features=1.0, min_samples_split=2, n_jobs=1, random_state=None)</span>
</code></dt>
<dd>
<div class="desc"><p>An easy-interpretable classifier optimizing simple logical rules.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>feature_names</code></strong> :&ensp;<code>list</code> of <code>str</code>, optional</dt>
<dd>The names of each feature to be used for returning rules in string
format.</dd>
<dt><strong><code>precision_min</code></strong> :&ensp;<code>float</code>, optional <code>(default=0.5)</code></dt>
<dd>The minimal precision of a rule to be selected.</dd>
<dt><strong><code>recall_min</code></strong> :&ensp;<code>float</code>, optional <code>(default=0.01)</code></dt>
<dd>The minimal recall of a rule to be selected.</dd>
<dt><strong><code>n_estimators</code></strong> :&ensp;<code>int</code>, optional <code>(default=10)</code></dt>
<dd>The number of base estimators (rules) to use for prediction. More are
built before selection. All are available in the estimators_ attribute.</dd>
<dt><strong><code>max_samples</code></strong> :&ensp;<code>int</code> or <code>float</code>, optional <code>(default=.8)</code></dt>
<dd>The number of samples to draw from X to train each decision tree, from
which rules are generated and selected.
- If int, then draw <code>max_samples</code> samples.
- If float, then draw <code>max_samples * X.shape[0]</code> samples.
If max_samples is larger than the number of samples provided,
all samples will be used for all trees (no sampling).</dd>
<dt><strong><code>max_samples_features</code></strong> :&ensp;<code>int</code> or <code>float</code>, optional <code>(default=1.0)</code></dt>
<dd>The number of features to draw from X to train each decision tree, from
which rules are generated and selected.
- If int, then draw <code>max_features</code> features.
- If float, then draw <code>max_features * X.shape[1]</code> features.</dd>
<dt><strong><code>bootstrap</code></strong> :&ensp;<code>boolean</code>, optional <code>(default=False)</code></dt>
<dd>Whether samples are drawn with replacement.</dd>
<dt><strong><code>bootstrap_features</code></strong> :&ensp;<code>boolean</code>, optional <code>(default=False)</code></dt>
<dd>Whether features are drawn with replacement.</dd>
<dt><strong><code>max_depth</code></strong> :&ensp;<code>integer</code> or <code>List</code> or <code>None</code>, optional <code>(default=3)</code></dt>
<dd>The maximum depth of the decision trees. If None, then nodes are
expanded until all leaves are pure or until all leaves contain less
than min_samples_split samples.
If an iterable is passed, you will train n_estimators
for each tree depth. It allows you to create and compare
rules of different length.</dd>
<dt><strong><code>max_depth_duplication</code></strong> :&ensp;<code>integer</code>, optional <code>(default=None)</code></dt>
<dd>The maximum depth of the decision tree for rule deduplication,
if None then no deduplication occurs.</dd>
<dt><strong><code>max_features</code></strong> :&ensp;<code>int, float, string</code> or <code>None</code>, optional <code>(default="auto")</code></dt>
<dd>
<p>The number of features considered (by each decision tree) when looking
for the best split:</p>
<ul>
<li>If int, then consider <code>max_features</code> features at each split.</li>
<li>If float, then <code>max_features</code> is a percentage and
<code>int(max_features * n_features)</code> features are considered at each
split.</li>
<li>If "auto", then <code>max_features=sqrt(n_features)</code>.</li>
<li>If "sqrt", then <code>max_features=sqrt(n_features)</code> (same as "auto").</li>
<li>If "log2", then <code>max_features=log2(n_features)</code>.</li>
<li>If None, then <code>max_features=n_features</code>.</li>
</ul>
<p>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 <code>max_features</code> features.</p>
</dd>
<dt><strong><code>min_samples_split</code></strong> :&ensp;<code>int, float</code>, optional <code>(default=2)</code></dt>
<dd>The minimum number of samples required to split an internal node for
each decision tree.
- If int, then consider <code>min_samples_split</code> as the minimum number.
- If float, then <code>min_samples_split</code> is a percentage and
<code>ceil(min_samples_split * n_samples)</code> are the minimum
number of samples for each split.</dd>
<dt><strong><code>n_jobs</code></strong> :&ensp;<code>integer</code>, optional <code>(default=1)</code></dt>
<dd>The number of jobs to run in parallel for both <code>fit</code> and <code>predict</code>.
If -1, then the number of jobs is set to the number of cores.</dd>
<dt><strong><code>random_state</code></strong> :&ensp;<code>int, RandomState instance</code> or <code>None</code>, optional</dt>
<dd>
<ul>
<li>If int, random_state is the seed used by the random number generator.</li>
<li>If RandomState instance, random_state is the random number generator.</li>
<li>If None, the random number generator is the RandomState instance used
by <code>np.random</code>.</li>
</ul>
</dd>
<dt><strong><code>verbose</code></strong> :&ensp;<code>int</code>, optional <code>(default=0)</code></dt>
<dd>Controls the verbosity of the tree building process.</dd>
</dl>
<h2 id="attributes">Attributes</h2>
<p>rules_ : dict of tuples (rule, precision, recall, nb).
The collection of <code>n_estimators</code> rules used in the <code>predict</code> method.
The rules are generated by fitted sub-estimators (decision trees). Each
rule satisfies recall_min and precision_min conditions. The selection
is done according to OOB precisions.</p>
<dl>
<dt><strong><code>estimators_</code></strong> :&ensp;<code>list</code> of <code>DecisionTreeClassifier</code></dt>
<dd>The collection of fitted sub-estimators used to generate candidate
rules.</dd>
<dt><strong><code>estimators_samples_</code></strong> :&ensp;<code>list</code> of <code>arrays</code></dt>
<dd>The subset of drawn samples (i.e., the in-bag samples) for each base
estimator.</dd>
<dt><strong><code>estimators_features_</code></strong> :&ensp;<code>list</code> of <code>arrays</code></dt>
<dd>The subset of drawn features for each base estimator.</dd>
<dt><strong><code>max_samples_</code></strong> :&ensp;<code>integer</code></dt>
<dd>The actual number of samples</dd>
<dt><strong><code>n_features_</code></strong> :&ensp;<code>integer</code></dt>
<dd>The number of features when <code>fit</code> is performed.</dd>
<dt><strong><code>classes_</code></strong> :&ensp;<code>array, shape (n_classes,)</code></dt>
<dd>The classes labels.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class FPSkopeClassifier(SkopeRulesClassifier):

    def __init__(self,
                 minsupport=0.1,
                 maxcardinality=2,
                 verbose=False,
                 precision_min=0.5,
                 recall_min=0.01,
                 n_estimators=10,
                 max_samples=.8,
                 max_samples_features=1.,
                 bootstrap=False,
                 bootstrap_features=False,
                 max_depth=3,
                 max_depth_duplication=None,
                 max_features=1.,
                 min_samples_split=2,
                 n_jobs=1,
                 random_state=None):
        super().__init__(precision_min,
                         recall_min,
                         n_estimators,
                         max_samples,
                         max_samples_features,
                         bootstrap,
                         bootstrap_features,
                         max_depth,
                         max_depth_duplication,
                         max_features,
                         min_samples_split,
                         n_jobs,
                         random_state,
                         verbose)
        self.minsupport = minsupport
        self.maxcardinality = maxcardinality
        self.verbose = verbose

    def fit(self, X, y=None, feature_names=None, undiscretized_features=[], sample_weight=None):
        self.undiscretized_features = undiscretized_features
        super().fit(X, y, feature_names=feature_names, sample_weight=sample_weight)
        return self

    def _extract_rules(self, X, y) -&gt; List[str]:
        X = pd.DataFrame(X, columns=self.feature_placeholders)
        itemsets = extract_fpgrowth(X, minsupport=self.minsupport,
                                    maxcardinality=self.maxcardinality,
                                    verbose=self.verbose)
        return [itemsets_to_rules(itemsets)], [np.arange(X.shape[0])], [np.arange(len(self.feature_names))]

    def _score_rules(self, X, y, rules) -&gt; List[Rule]:
        return score_precision_recall(X, y,
                                      rules,
                                      self.estimators_samples_,
                                      self.estimators_features_,
                                      self.feature_placeholders,
                                      oob=False)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="imodels.rule_set.skope_rules.SkopeRulesClassifier" href="skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier">SkopeRulesClassifier</a></li>
<li>sklearn.base.BaseEstimator</li>
<li>sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin</li>
<li>sklearn.utils._metadata_requests._MetadataRequester</li>
<li><a title="imodels.rule_set.rule_set.RuleSet" href="rule_set.html#imodels.rule_set.rule_set.RuleSet">RuleSet</a></li>
<li>sklearn.base.ClassifierMixin</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="imodels.rule_set.fpskope.FPSkopeClassifier.set_fit_request"><code class="name flex">
<span>def <span class="ident">set_fit_request</span></span>(<span>self: <a title="imodels.rule_set.fpskope.FPSkopeClassifier" href="#imodels.rule_set.fpskope.FPSkopeClassifier">FPSkopeClassifier</a>, *, feature_names: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', undiscretized_features: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> <a title="imodels.rule_set.fpskope.FPSkopeClassifier" href="#imodels.rule_set.fpskope.FPSkopeClassifier">FPSkopeClassifier</a></span>
</code></dt>
<dd>
<div class="desc"><p>Request metadata passed to the <code>fit</code> method.</p>
<p>Note that this method is only relevant if
<code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>).
Please see :ref:<code>User Guide &lt;metadata_routing&gt;</code> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul>
<li>
<p><code>True</code>: metadata is requested, and passed to <code>fit</code> if provided. The request is ignored if metadata is not provided.</p>
</li>
<li>
<p><code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>fit</code>.</p>
</li>
<li>
<p><code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p>
</li>
<li>
<p><code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p>
</li>
</ul>
<p>The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="admonition versionadded">
<p class="admonition-title">Added in version:&ensp;1.3</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p>
</div>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>feature_names</code></strong> :&ensp;<code>str, True, False,</code> or <code>None</code>,
default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
<dd>Metadata routing for <code>feature_names</code> parameter in <code>fit</code>.</dd>
<dt><strong><code>sample_weight</code></strong> :&ensp;<code>str, True, False,</code> or <code>None</code>,
default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
<dd>Metadata routing for <code>sample_weight</code> parameter in <code>fit</code>.</dd>
<dt><strong><code>undiscretized_features</code></strong> :&ensp;<code>str, True, False,</code> or <code>None</code>,
default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
<dd>Metadata routing for <code>undiscretized_features</code> parameter in <code>fit</code>.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>self</code></strong> :&ensp;<code>object</code></dt>
<dd>The updated object.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def func(**kw):
    &#34;&#34;&#34;Updates the request for provided parameters

    This docstring is overwritten below.
    See REQUESTER_DOC for expected functionality
    &#34;&#34;&#34;
    if not _routing_enabled():
        raise RuntimeError(
            &#34;This method is only available when metadata routing is enabled.&#34;
            &#34; You can enable it using&#34;
            &#34; sklearn.set_config(enable_metadata_routing=True).&#34;
        )

    if self.validate_keys and (set(kw) - set(self.keys)):
        raise TypeError(
            f&#34;Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments&#34;
            f&#34; are: {set(self.keys)}&#34;
        )

    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</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="imodels.rule_set.skope_rules.SkopeRulesClassifier" href="skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier">SkopeRulesClassifier</a></b></code>:
<ul class="hlist">
<li><code><a title="imodels.rule_set.skope_rules.SkopeRulesClassifier.fit" href="skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier.fit">fit</a></code></li>
<li><code><a title="imodels.rule_set.skope_rules.SkopeRulesClassifier.predict" href="skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier.predict">predict</a></code></li>
<li><code><a title="imodels.rule_set.skope_rules.SkopeRulesClassifier.predict_proba" href="skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier.predict_proba">predict_proba</a></code></li>
<li><code><a title="imodels.rule_set.skope_rules.SkopeRulesClassifier.set_score_request" href="skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier.set_score_request">set_score_request</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
</article>
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<h1>Index 🔍</h1>
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<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="imodels.rule_set" href="index.html">imodels.rule_set</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="imodels.rule_set.fpskope.FPSkopeClassifier" href="#imodels.rule_set.fpskope.FPSkopeClassifier">FPSkopeClassifier</a></code></h4>
<ul class="">
<li><code><a title="imodels.rule_set.fpskope.FPSkopeClassifier.set_fit_request" href="#imodels.rule_set.fpskope.FPSkopeClassifier.set_fit_request">set_fit_request</a></code></li>
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