diff --git a/.ipynb_checkpoints/test-checkpoint.ipynb b/.ipynb_checkpoints/test-checkpoint.ipynb
index 48910d4..e92fe3c 100644
--- a/.ipynb_checkpoints/test-checkpoint.ipynb
+++ b/.ipynb_checkpoints/test-checkpoint.ipynb
@@ -190,25 +190,25 @@
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@@ -373,210 +373,237 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 17,
+ "id": "2840a103-99fb-466f-ae75-45e11c1b9c5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import cross_validate, StratifiedKFold, KFold, cross_val_score\n",
+ "import numpy as np\n",
+ "n_folds = 5\n",
+ "score_name = \"accuracy\"\n",
+ "random_state=17\n",
+ "def validate_classifier(model, X, y, stratified, fit_params):\n",
+ " stratified_class = StratifiedKFold if stratified else KFold\n",
+ " kfold = stratified_class(shuffle=True, random_state=random_state, n_splits=n_folds)\n",
+ " #return cross_validate(model, X, y, cv=kfold, return_estimator=True, scoring=score_name)\n",
+ " return cross_val_score(model, X, y, fit_params=fit_params)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
"id": "6a1aad95-370f-4854-ae9a-32205aff5d39",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b7c115c93e41439fa707a7a53d4e09de",
+ "model_id": "b620372c05294afc853885da0848e389",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/210 [00:00"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n"
+ ]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "42459beb4c964bd9a7e42993c315407e",
+ "model_id": "ad33ee9f224d4abfa9a23338f07b32f2",
"version_major": 2,
"version_minor": 0
},
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- " 0%| | 0/210 [00:00"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n"
+ ]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "56c8d86e92d84b6a900554896791e2f8",
+ "model_id": "fa05948fc73d43da8a3f01adc71c5e53",
"version_major": 2,
"version_minor": 0
},
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- " 0%| | 0/210 [00:00"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n"
+ ]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "fbb1f212cc4f4977bf820f589d706f10",
+ "model_id": "1b67f117ceed43818c98342221b11184",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/210 [00:00"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n"
+ ]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2ba8efb4ba7c4104bc301b57bd8e6a74",
+ "model_id": "2399fb4327a242f0bd1c3e64b84382a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/210 [00:00"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n",
+ "/Users/rmontanana/Code/pgmpy/pgmpy/factors/discrete/DiscreteFactor.py:541: UserWarning: Found unknown state name. Trying to switch to using all state names as state numbers\n",
+ " warnings.warn(\n"
+ ]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
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- "version_major": 2,
- "version_minor": 0
- },
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- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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- "metadata": {},
- "output_type": "display_data"
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- {
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- },
- "metadata": {},
- "output_type": "display_data"
+ "ename": "IndexError",
+ "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn [19], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m head \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m4\u001b[39m):\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m#model.fit(X, y, head=head, features=features, class_name=class_name)\u001b[39;00m\n\u001b[1;32m 8\u001b[0m score \u001b[38;5;241m=\u001b[39m validate_classifier(model, X, y, stratified\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, fit_params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(head\u001b[38;5;241m=\u001b[39mhead, features\u001b[38;5;241m=\u001b[39mfeatures, class_name\u001b[38;5;241m=\u001b[39mclass_name))\n\u001b[0;32m----> 9\u001b[0m model\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimple_init=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msimple_init\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m head=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhead\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m score=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mmean(score[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest_score\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+ "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
+ ]
}
],
"source": [
+ "import warnings\n",
+ "from stree import Stree\n",
+ "warnings.filterwarnings('ignore')\n",
"for simple_init in [False, True]:\n",
" model = TAN(simple_init=simple_init)\n",
" for head in range(4):\n",
- " model.fit(X, y, head=head, features=features, class_name=class_name)\n",
- " model.plot(f\"simple_init={simple_init} head={head} score={model.score(X, y)}\")"
+ " #model.fit(X, y, head=head, features=features, class_name=class_name)\n",
+ " score = validate_classifier(model, X, y, stratified=False, fit_params=dict(head=head, features=features, class_name=class_name))\n",
+ " #model.plot(f\"simple_init={simple_init} head={head} score={np.mean(score['test_score'])}\")\n",
+ " model.plot(f\"simple_init={simple_init} head={head} score={np.mean(score)}\")"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c389ff1e-76d9-4c5b-9860-ea6d4752fac7",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c58629f-000b-4d8c-8896-efd032f1090c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/bayesclass/bayesclass.py b/bayesclass/bayesclass.py
index 17e14d3..63cade9 100644
--- a/bayesclass/bayesclass.py
+++ b/bayesclass/bayesclass.py
@@ -1,12 +1,18 @@
"""
This is a module to be used as a reference for building other modules
"""
+import random
+from itertools import combinations
import pandas as pd
from sklearn.base import ClassifierMixin, BaseEstimator
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
import networkx as nx
-from pgmpy.estimators import TreeSearch, BayesianEstimator
+from pgmpy.estimators import (
+ TreeSearch,
+ BayesianEstimator,
+ MaximumLikelihoodEstimator,
+)
from pgmpy.models import BayesianNetwork
import matplotlib.pyplot as plt
@@ -29,9 +35,12 @@ class TAN(ClassifierMixin, BaseEstimator):
The classes seen at :meth:`fit`.
"""
- def __init__(self, simple_init=False, show_progress=False):
+ def __init__(
+ self, simple_init=False, show_progress=False, random_state=None
+ ):
self.simple_init = simple_init
self.show_progress = show_progress
+ self.random_state = random_state
def fit(self, X, y, **kwargs):
"""A reference implementation of a fitting function for a classifier.
@@ -44,7 +53,8 @@ class TAN(ClassifierMixin, BaseEstimator):
**kwargs : dict
class_name : str (default='class') Name of the class column
features: list (default=None) List of features
- head: int (default=0) Index of the head node
+ head: int (default=None) Index of the head node. Default value
+ gets the node with the highest sum of weights (mutual_info)
Returns
-------
self : object
@@ -57,20 +67,22 @@ class TAN(ClassifierMixin, BaseEstimator):
# Default values
self.class_name_ = "class"
self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
- self.head_ = 0
+ self.head_ = None
expected_args = ["class_name", "features", "head"]
for key, value in kwargs.items():
if key in expected_args:
setattr(self, f"{key}_", value)
-
else:
raise ValueError(f"Unexpected argument: {key}")
-
+ if self.random_state is not None:
+ random.seed(self.random_state)
+ if self.head_ == "random":
+ self.head_ = random.randint(0, len(self.features_) - 1)
if len(self.features_) != X.shape[1]:
raise ValueError(
"Number of features does not match the number of columns in X"
)
- if self.head_ >= len(self.features_):
+ if self.head_ is not None and self.head_ >= len(self.features_):
raise ValueError("Head index out of range")
self.X_ = X
@@ -80,37 +92,57 @@ class TAN(ClassifierMixin, BaseEstimator):
return self
def __initial_edges(self):
+ """As with the naive Bayes, in a TAN structure, the class has no
+ parents, while features must have the class as parent and are forced to
+ have one other feature as parent too (except for one single feature,
+ which has only the class as parent and is considered the root of the
+ features' tree)
+ Cassio P. de Campos, Giorgio Corani, Mauro Scanagatta, Marco Cuccu,
+ Marco Zaffalon,
+ Learning extended tree augmented naive structures,
+ International Journal of Approximate Reasoning,
+ Returns
+ -------
+ List
+ List of edges
+ """
+ head = 0 if self.head_ is None else self.head_
if self.simple_init:
- first_node = self.features_[self.head_]
+ first_node = self.features_[head]
return [
(first_node, feature)
for feature in self.features_
if feature != first_node
]
- edges = []
- for i in range(len(self.features_)):
- for j in range(i + 1, len(self.features_)):
- edges.append((self.features_[i], self.features_[j]))
- return edges
+ # initialize a complete network with all edges starting from head
+ reordered = [
+ self.features_[idx % len(self.features_)]
+ for idx in range(head, len(self.features_) + head)
+ ]
+ return list(combinations(reordered, 2))
def __train(self):
+ # Initialize a Naive Bayes model
net = [(self.class_name_, feature) for feature in self.features_]
self.model_ = BayesianNetwork(net)
# initialize a complete network with all edges
self.model_.add_edges_from(self.__initial_edges())
-
self.dataset_ = pd.DataFrame(self.X_, columns=self.features_)
self.dataset_[self.class_name_] = self.y_
# learn graph structure
- est = TreeSearch(self.dataset_, root_node=self.features_[self.head_])
+ root_node = None if self.head_ is None else self.features_[self.head_]
+ est = TreeSearch(self.dataset_, root_node=root_node)
dag = est.estimate(
estimator_type="tan",
class_node=self.class_name_,
show_progress=self.show_progress,
)
+ if self.head_ is None:
+ self.head_ = est.root_node
self.model_ = BayesianNetwork(dag.edges())
self.model_.fit(
self.dataset_,
+ # estimator=MaximumLikelihoodEstimator,
estimator=BayesianEstimator,
prior_type="K2",
)
diff --git a/bayesclass/tests/test_bayesclass.py b/bayesclass/tests/test_bayesclass.py
index 24a8dd6..c805878 100644
--- a/bayesclass/tests/test_bayesclass.py
+++ b/bayesclass/tests/test_bayesclass.py
@@ -16,12 +16,26 @@ def data():
return enc.fit_transform(X), y
-def test_TAN_classifier(data):
+def test_TAN_constructor():
clf = TAN()
-
# Test default values of hyperparameters
assert not clf.simple_init
assert not clf.show_progress
+ assert clf.random_state is None
+ clf = TAN(simple_init=True, show_progress=True, random_state=17)
+ assert clf.simple_init
+ assert clf.show_progress
+ assert clf.random_state == 17
+
+
+def test_TAN_random_head(data):
+ clf = TAN(random_state=17)
+ clf.fit(*data, head="random")
+ assert clf.head_ == 3
+
+
+def test_TAN_classifier(data):
+ clf = TAN()
clf.fit(*data)
attribs = ["classes_", "X_", "y_", "head_", "features_", "class_name_"]
diff --git a/test.ipynb b/test.ipynb
index d59983b..6a42616 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -189,39 +189,39 @@
"
\n",
" \n",
" | 0 | \n",
- " 31.0 | \n",
- " 8.0 | \n",
- " 15.0 | \n",
- " 13.0 | \n",
+ " 30.0 | \n",
+ " 14.0 | \n",
+ " 16.0 | \n",
+ " 18.0 | \n",
" 38.0 | \n",
- " 26.0 | \n",
- " 9.0 | \n",
+ " 32.0 | \n",
+ " 14.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0 | \n",
"
\n",
" \n",
" | 1 | \n",
- " 23.0 | \n",
+ " 17.0 | \n",
" 3.0 | \n",
- " 15.0 | \n",
- " 19.0 | \n",
- " 36.0 | \n",
- " 19.0 | \n",
- " 9.0 | \n",
+ " 18.0 | \n",
+ " 21.0 | \n",
+ " 34.0 | \n",
+ " 24.0 | \n",
+ " 10.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1 | \n",
"
\n",
" \n",
" | 2 | \n",
- " 31.0 | \n",
- " 17.0 | \n",
- " 15.0 | \n",
- " 20.0 | \n",
+ " 30.0 | \n",
" 24.0 | \n",
- " 21.0 | \n",
- " 7.0 | \n",
+ " 15.0 | \n",
+ " 22.0 | \n",
+ " 22.0 | \n",
+ " 27.0 | \n",
+ " 6.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0 | \n",
@@ -229,9 +229,9 @@
"
\n",
" | 3 | \n",
" 3.0 | \n",
- " 42.0 | \n",
+ " 51.0 | \n",
" 6.0 | \n",
- " 21.0 | \n",
+ " 23.0 | \n",
" 47.0 | \n",
" 0.0 | \n",
" 3.0 | \n",
@@ -241,15 +241,15 @@
"
\n",
" \n",
" | 4 | \n",
- " 63.0 | \n",
+ " 62.0 | \n",
" 4.0 | \n",
" 0.0 | \n",
- " 11.0 | \n",
+ " 13.0 | \n",
" 0.0 | \n",
" 8.0 | \n",
- " 21.0 | \n",
+ " 30.0 | \n",
" 0.0 | \n",
- " 4.0 | \n",
+ " 5.0 | \n",
" 3 | \n",
"
\n",
" \n",
@@ -267,12 +267,12 @@
"
\n",
" \n",
" | 209 | \n",
- " 17.0 | \n",
- " 22.0 | \n",
- " 14.0 | \n",
- " 15.0 | \n",
- " 26.0 | \n",
- " 21.0 | \n",
+ " 13.0 | \n",
+ " 33.0 | \n",
+ " 11.0 | \n",
+ " 19.0 | \n",
+ " 23.0 | \n",
+ " 27.0 | \n",
" 4.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
@@ -280,12 +280,12 @@
"
\n",
" \n",
" | 210 | \n",
- " 14.0 | \n",
- " 10.0 | \n",
- " 15.0 | \n",
- " 27.0 | \n",
- " 25.0 | \n",
- " 30.0 | \n",
+ " 11.0 | \n",
+ " 19.0 | \n",
+ " 18.0 | \n",
+ " 29.0 | \n",
+ " 23.0 | \n",
+ " 33.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
@@ -293,39 +293,39 @@
"
\n",
" \n",
" | 211 | \n",
- " 19.0 | \n",
- " 33.0 | \n",
- " 15.0 | \n",
- " 17.0 | \n",
- " 36.0 | \n",
- " 12.0 | \n",
+ " 14.0 | \n",
+ " 41.0 | \n",
+ " 18.0 | \n",
+ " 20.0 | \n",
+ " 34.0 | \n",
+ " 14.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
- " 4.0 | \n",
+ " 5.0 | \n",
" 3 | \n",
"
\n",
" \n",
" | 212 | \n",
- " 23.0 | \n",
- " 5.0 | \n",
+ " 20.0 | \n",
" 8.0 | \n",
- " 21.0 | \n",
- " 43.0 | \n",
- " 30.0 | \n",
- " 9.0 | \n",
+ " 8.0 | \n",
+ " 23.0 | \n",
+ " 42.0 | \n",
+ " 33.0 | \n",
+ " 11.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 3 | \n",
"
\n",
" \n",
" | 213 | \n",
- " 44.0 | \n",
- " 38.0 | \n",
+ " 43.0 | \n",
+ " 46.0 | \n",
" 6.0 | \n",
- " 21.0 | \n",
- " 25.0 | \n",
+ " 23.0 | \n",
+ " 23.0 | \n",
" 0.0 | \n",
- " 10.0 | \n",
+ " 15.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 2 | \n",
@@ -337,17 +337,17 @@
],
"text/plain": [
" RI Na Mg Al Si 'K' Ca Ba Fe Type\n",
- "0 31.0 8.0 15.0 13.0 38.0 26.0 9.0 0.0 0.0 0\n",
- "1 23.0 3.0 15.0 19.0 36.0 19.0 9.0 0.0 0.0 1\n",
- "2 31.0 17.0 15.0 20.0 24.0 21.0 7.0 0.0 0.0 0\n",
- "3 3.0 42.0 6.0 21.0 47.0 0.0 3.0 0.0 0.0 2\n",
- "4 63.0 4.0 0.0 11.0 0.0 8.0 21.0 0.0 4.0 3\n",
+ "0 30.0 14.0 16.0 18.0 38.0 32.0 14.0 0.0 0.0 0\n",
+ "1 17.0 3.0 18.0 21.0 34.0 24.0 10.0 0.0 0.0 1\n",
+ "2 30.0 24.0 15.0 22.0 22.0 27.0 6.0 0.0 0.0 0\n",
+ "3 3.0 51.0 6.0 23.0 47.0 0.0 3.0 0.0 0.0 2\n",
+ "4 62.0 4.0 0.0 13.0 0.0 8.0 30.0 0.0 5.0 3\n",
".. ... ... ... ... ... ... ... ... ... ...\n",
- "209 17.0 22.0 14.0 15.0 26.0 21.0 4.0 0.0 0.0 1\n",
- "210 14.0 10.0 15.0 27.0 25.0 30.0 3.0 0.0 0.0 3\n",
- "211 19.0 33.0 15.0 17.0 36.0 12.0 3.0 0.0 4.0 3\n",
- "212 23.0 5.0 8.0 21.0 43.0 30.0 9.0 0.0 0.0 3\n",
- "213 44.0 38.0 6.0 21.0 25.0 0.0 10.0 0.0 0.0 2\n",
+ "209 13.0 33.0 11.0 19.0 23.0 27.0 4.0 0.0 0.0 1\n",
+ "210 11.0 19.0 18.0 29.0 23.0 33.0 3.0 0.0 0.0 3\n",
+ "211 14.0 41.0 18.0 20.0 34.0 14.0 3.0 0.0 5.0 3\n",
+ "212 20.0 8.0 8.0 23.0 42.0 33.0 11.0 0.0 0.0 3\n",
+ "213 43.0 46.0 6.0 23.0 23.0 0.0 15.0 0.0 0.0 2\n",
"\n",
"[214 rows x 10 columns]"
]
@@ -373,39 +373,317 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "6a1aad95-370f-4854-ae9a-32205aff5d39",
+ "execution_count": 17,
+ "id": "2840a103-99fb-466f-ae75-45e11c1b9c5a",
"metadata": {},
"outputs": [],
"source": [
- "for simple_init in [False, True]:\n",
- " model = TAN(simple_init=simple_init)\n",
- " for head in range(4):\n",
- " model.fit(X, y, head=head, features=features, class_name=class_name)\n",
- " ypred = model.predict(X)\n",
- " #model.plot(f\"simple_init={simple_init} head={head} score={model.predict(X)}\")"
+ "from sklearn.model_selection import cross_validate, StratifiedKFold, KFold, cross_val_score\n",
+ "import numpy as np\n",
+ "n_folds = 5\n",
+ "score_name = \"accuracy\"\n",
+ "random_state=17\n",
+ "def validate_classifier(model, X, y, stratified, fit_params):\n",
+ " stratified_class = StratifiedKFold if stratified else KFold\n",
+ " kfold = stratified_class(shuffle=True, random_state=random_state, n_splits=n_folds)\n",
+ " #return cross_validate(model, X, y, cv=kfold, return_estimator=True, scoring=score_name)\n",
+ " return cross_val_score(model, X, y, fit_params=fit_params)"
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "76905bf3",
+ "execution_count": 20,
+ "id": "6a1aad95-370f-4854-ae9a-32205aff5d39",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7bdb666c5e5140e688141356958b362f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/43 [00:00 10\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msimple_init=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msimple_init\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m head=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mhead\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m score=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscore\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Code/bayesclass/bayesclass/bayesclass.py:148\u001b[0m, in \u001b[0;36mTAN.plot\u001b[0;34m(self, title)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 147\u001b[0m nx\u001b[38;5;241m.\u001b[39mdraw_circular(\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_\u001b[49m,\n\u001b[1;32m 149\u001b[0m with_labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 150\u001b[0m arrowsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m,\n\u001b[1;32m 151\u001b[0m node_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m800\u001b[39m,\n\u001b[1;32m 152\u001b[0m alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.3\u001b[39m,\n\u001b[1;32m 153\u001b[0m font_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbold\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 154\u001b[0m )\n\u001b[1;32m 155\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(title)\n\u001b[1;32m 156\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'TAN' object has no attribute 'model_'"
+ ]
+ }
+ ],
+ "source": [
+ "import warnings\n",
+ "from stree import Stree\n",
+ "warnings.filterwarnings('ignore')\n",
+ "for simple_init in [False, True]:\n",
+ " model = TAN(simple_init=simple_init)\n",
+ " for head in range(4):\n",
+ " #model.fit(X, y, head=head, features=features, class_name=class_name)\n",
+ " score = validate_classifier(model, X, y, stratified=False, fit_params=dict(head=head, features=features, class_name=class_name))\n",
+ " #model.plot(f\"simple_init={simple_init} head={head} score={np.mean(score['test_score'])}\")\n",
+ " model.plot(f\"simple_init={simple_init} head={head} score={np.mean(score)}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "c389ff1e-76d9-4c5b-9860-ea6d4752fac7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(214, 9)"
+ "array([nan, nan, nan, nan, nan])"
]
},
- "execution_count": 5,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "X.shape\n"
+ "score"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "9c58629f-000b-4d8c-8896-efd032f1090c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "b 10\n",
+ "c 9\n",
+ "d 8\n",
+ "e 7\n",
+ "a 6\n"
+ ]
+ }
+ ],
+ "source": [
+ "from queue import PriorityQueue\n",
+ "q = PriorityQueue()\n",
+ "lista = ['b', 'c', 'd', 'e', 'a']\n",
+ "for i, c in zip(lista, range(len(lista))):\n",
+ " print(i,10-c)\n",
+ " q.put(i,10-c)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "e2a768c0-3e21-48f3-b118-25408122d01c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a\n",
+ "b\n",
+ "c\n",
+ "d\n",
+ "e\n"
+ ]
+ }
+ ],
+ "source": [
+ "while not q.empty():\n",
+ " print(q.get())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96bb1acd-f450-4b9c-8f54-f020e23dfc14",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {