Binary quantifier added, support added and tested.
This commit is contained in:
Lorenzo Volpi
parent
b969234244
commit
1347ac3c9d
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@ -2,3 +2,4 @@
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quavenv/*
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*.pdf
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quacc/__pycache__/*
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tests/__pycache__/*
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@ -0,0 +1,16 @@
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@ -1,10 +1,9 @@
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@ -20,7 +19,6 @@ scipy = ">=1.1.0"
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@ -32,7 +30,6 @@ files = [
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@ -141,11 +136,21 @@ ufo = ["fs (>=2.2.0,<3)"]
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|
||||
sql-other = ["SQLAlchemy (>=1.4.16)"]
|
||||
test = ["hypothesis (>=6.34.2)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
|
||||
test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
|
||||
xml = ["lxml (>=4.6.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "pillow"
|
||||
version = "9.5.0"
|
||||
description = "Python Imaging Library (Fork)"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
|
|
@ -490,11 +489,25 @@ files = [
|
|||
docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
|
||||
tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
name = "pluggy"
|
||||
version = "1.2.0"
|
||||
description = "plugin and hook calling mechanisms for python"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pluggy-1.2.0-py3-none-any.whl", hash = "sha256:c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849"},
|
||||
{file = "pluggy-1.2.0.tar.gz", hash = "sha256:d12f0c4b579b15f5e054301bb226ee85eeeba08ffec228092f8defbaa3a4c4b3"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
dev = ["pre-commit", "tox"]
|
||||
testing = ["pytest", "pytest-benchmark"]
|
||||
|
||||
[[package]]
|
||||
name = "pyparsing"
|
||||
version = "3.0.9"
|
||||
description = "pyparsing module - Classes and methods to define and execute parsing grammars"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6.8"
|
||||
files = [
|
||||
|
|
@ -505,11 +518,30 @@ files = [
|
|||
[package.extras]
|
||||
diagrams = ["jinja2", "railroad-diagrams"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest"
|
||||
version = "7.4.0"
|
||||
description = "pytest: simple powerful testing with Python"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pytest-7.4.0-py3-none-any.whl", hash = "sha256:78bf16451a2eb8c7a2ea98e32dc119fd2aa758f1d5d66dbf0a59d69a3969df32"},
|
||||
{file = "pytest-7.4.0.tar.gz", hash = "sha256:b4bf8c45bd59934ed84001ad51e11b4ee40d40a1229d2c79f9c592b0a3f6bd8a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "sys_platform == \"win32\""}
|
||||
iniconfig = "*"
|
||||
packaging = "*"
|
||||
pluggy = ">=0.12,<2.0"
|
||||
|
||||
[package.extras]
|
||||
testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
|
||||
|
||||
[[package]]
|
||||
name = "python-dateutil"
|
||||
version = "2.8.2"
|
||||
description = "Extensions to the standard Python datetime module"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
|
||||
files = [
|
||||
|
|
@ -524,7 +556,6 @@ six = ">=1.5"
|
|||
name = "pytz"
|
||||
version = "2023.3"
|
||||
description = "World timezone definitions, modern and historical"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
|
|
@ -536,7 +567,6 @@ files = [
|
|||
name = "quapy"
|
||||
version = "0.1.7"
|
||||
description = "QuaPy: a framework for Quantification in Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6, <4"
|
||||
files = [
|
||||
|
|
@ -557,7 +587,6 @@ xlrd = "*"
|
|||
name = "scikit-learn"
|
||||
version = "1.2.2"
|
||||
description = "A set of python modules for machine learning and data mining"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
|
|
@ -600,7 +629,6 @@ tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.3)", "mypy (
|
|||
name = "scipy"
|
||||
version = "1.9.3"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
|
|
@ -639,7 +667,6 @@ test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "sciki
|
|||
name = "six"
|
||||
version = "1.16.0"
|
||||
description = "Python 2 and 3 compatibility utilities"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||
files = [
|
||||
|
|
@ -651,7 +678,6 @@ files = [
|
|||
name = "threadpoolctl"
|
||||
version = "3.1.0"
|
||||
description = "threadpoolctl"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
|
|
@ -663,7 +689,6 @@ files = [
|
|||
name = "tqdm"
|
||||
version = "4.65.0"
|
||||
description = "Fast, Extensible Progress Meter"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
|
|
@ -684,7 +709,6 @@ telegram = ["requests"]
|
|||
name = "tzdata"
|
||||
version = "2023.3"
|
||||
description = "Provider of IANA time zone data"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
files = [
|
||||
|
|
@ -696,7 +720,6 @@ files = [
|
|||
name = "xlrd"
|
||||
version = "2.0.1"
|
||||
description = "Library for developers to extract data from Microsoft Excel (tm) .xls spreadsheet files"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
|
||||
files = [
|
||||
|
|
@ -712,4 +735,4 @@ test = ["pytest", "pytest-cov"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "811aa60aea2ab4cf9b4bc4ad3e546e8ee1a81d78f15acd35f3f736b5f97512b4"
|
||||
content-hash = "834ffb619893a1fb006e1b5a3213cc772117c9000e719b95a4478f74fd5d0066"
|
||||
|
|
|
|||
|
|
@ -8,11 +8,15 @@ readme = "README.md"
|
|||
[tool.poetry.dependencies]
|
||||
python = "^3.11"
|
||||
quapy = "^0.1.7"
|
||||
pandas = "^2.0.3"
|
||||
|
||||
[tool.poetry.scripts]
|
||||
main = "quacc.main:main"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = "^7.4.0"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
|
|
|||
|
|
@ -1,11 +1,40 @@
|
|||
from typing import List, Optional
|
||||
from typing import Any, List, Optional
|
||||
|
||||
import numpy as np
|
||||
import math
|
||||
import quapy as qp
|
||||
import scipy.sparse as sp
|
||||
from quapy.data import LabelledCollection
|
||||
|
||||
|
||||
# Extended classes
|
||||
#
|
||||
# 0 ~ True 0
|
||||
# 1 ~ False 1
|
||||
# 2 ~ False 0
|
||||
# 3 ~ True 1
|
||||
# _____________________
|
||||
# | | |
|
||||
# | True 0 | False 1 |
|
||||
# |__________|__________|
|
||||
# | | |
|
||||
# | False 0 | True 1 |
|
||||
# |__________|__________|
|
||||
#
|
||||
class ExClassManager:
|
||||
@staticmethod
|
||||
def get_ex(n_classes: int, true_class: int, pred_class: int) -> int:
|
||||
return true_class * n_classes + pred_class
|
||||
|
||||
@staticmethod
|
||||
def get_pred(n_classes: int, ex_class: int) -> int:
|
||||
return ex_class % n_classes
|
||||
|
||||
@staticmethod
|
||||
def get_true(n_classes: int, ex_class: int) -> int:
|
||||
return ex_class // n_classes
|
||||
|
||||
|
||||
class ExtendedCollection(LabelledCollection):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -15,6 +44,67 @@ class ExtendedCollection(LabelledCollection):
|
|||
):
|
||||
super().__init__(instances, labels, classes=classes)
|
||||
|
||||
def split_by_pred(self):
|
||||
_ncl = int(math.sqrt(self.n_classes))
|
||||
_indexes = ExtendedCollection.split_index_by_pred(_ncl, self.instances)
|
||||
return [
|
||||
ExtendedCollection(
|
||||
self.instances[ind] if len(ind) > 0 else np.asarray([], dtype=int),
|
||||
np.asarray(
|
||||
[
|
||||
ExClassManager.get_true(_ncl, lbl)
|
||||
for lbl in (self.labels[ind] if len(ind) > 0 else [])
|
||||
],
|
||||
dtype=int,
|
||||
),
|
||||
classes=range(0, _ncl),
|
||||
)
|
||||
for ind in _indexes
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def split_index_by_pred(
|
||||
cls, n_classes: int, instances: np.ndarray
|
||||
) -> List[np.ndarray]:
|
||||
_pred_label = [np.argmax(inst[-n_classes:], axis=0) for inst in instances]
|
||||
return [
|
||||
np.asarray([j for (j, x) in enumerate(_pred_label) if x == i])
|
||||
for i in range(0, n_classes)
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def extend_instances(
|
||||
cls, instances: np.ndarray, pred_proba: np.ndarray
|
||||
) -> np.ndarray:
|
||||
if isinstance(instances, sp.csr_matrix):
|
||||
_pred_proba = sp.csr_matrix(pred_proba)
|
||||
n_x = sp.hstack([instances, _pred_proba])
|
||||
elif isinstance(instances, np.ndarray):
|
||||
n_x = np.concatenate((instances, pred_proba), axis=1)
|
||||
else:
|
||||
raise ValueError("Unsupported matrix format")
|
||||
|
||||
return n_x
|
||||
|
||||
@classmethod
|
||||
def extend_collection(cls, base: LabelledCollection, pred_proba: np.ndarray) -> Any:
|
||||
n_classes = base.n_classes
|
||||
|
||||
# n_X = [ X | predicted probs. ]
|
||||
n_x = cls.extend_instances(base.X, pred_proba)
|
||||
|
||||
# n_y = (exptected y, predicted y)
|
||||
pred = np.asarray([prob.argmax(axis=0) for prob in pred_proba])
|
||||
n_y = np.asarray(
|
||||
[
|
||||
ExClassManager.get_ex(n_classes, true_class, pred_class)
|
||||
for (true_class, pred_class) in zip(base.y, pred)
|
||||
]
|
||||
)
|
||||
|
||||
return ExtendedCollection(n_x, n_y, classes=[*range(0, n_classes * n_classes)])
|
||||
|
||||
|
||||
def get_dataset(name):
|
||||
datasets = {
|
||||
"spambase": lambda: qp.datasets.fetch_UCIDataset(
|
||||
|
|
|
|||
|
|
@ -1,11 +1,14 @@
|
|||
from abc import abstractmethod
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import scipy.sparse as sp
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.method.base import BaseQuantifier
|
||||
from quapy.method.aggregative import SLD
|
||||
from sklearn.base import BaseEstimator
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import cross_val_predict
|
||||
|
||||
from .data import ExtendedCollection
|
||||
from quacc.data import ExtendedCollection as EC
|
||||
|
||||
|
||||
def _check_prevalence_classes(true_classes, estim_classes, estim_prev):
|
||||
|
|
@ -15,60 +18,36 @@ def _check_prevalence_classes(true_classes, estim_classes, estim_prev):
|
|||
return estim_prev
|
||||
|
||||
|
||||
def _get_ex_class(classes, true_class, pred_class):
|
||||
return true_class * classes + pred_class
|
||||
|
||||
|
||||
def _extend_instances(instances, pred_proba):
|
||||
if isinstance(instances, sp.csr_matrix):
|
||||
_pred_proba = sp.csr_matrix(pred_proba)
|
||||
n_x = sp.hstack([instances, _pred_proba])
|
||||
elif isinstance(instances, np.ndarray):
|
||||
n_x = np.concatenate((instances, pred_proba), axis=1)
|
||||
else:
|
||||
raise ValueError("Unsupported matrix format")
|
||||
|
||||
return n_x
|
||||
|
||||
|
||||
def _extend_collection(base: LabelledCollection, pred_proba) -> ExtendedCollection:
|
||||
n_classes = base.n_classes
|
||||
|
||||
# n_X = [ X | predicted probs. ]
|
||||
n_x = _extend_instances(base.X, pred_proba)
|
||||
|
||||
# n_y = (exptected y, predicted y)
|
||||
pred = np.asarray([prob.argmax(axis=0) for prob in pred_proba])
|
||||
n_y = np.asarray(
|
||||
[
|
||||
_get_ex_class(n_classes, true_class, pred_class)
|
||||
for (true_class, pred_class) in zip(base.y, pred)
|
||||
]
|
||||
)
|
||||
|
||||
return ExtendedCollection(n_x, n_y, classes=[*range(0, n_classes * n_classes)])
|
||||
|
||||
|
||||
class AccuracyEstimator:
|
||||
def __init__(self, model: BaseEstimator, q_model: BaseQuantifier):
|
||||
self.model = model
|
||||
self.q_model = q_model
|
||||
self.e_train = None
|
||||
|
||||
def extend(self, base: LabelledCollection, pred_proba=None) -> ExtendedCollection:
|
||||
def extend(self, base: LabelledCollection, pred_proba=None) -> EC:
|
||||
if not pred_proba:
|
||||
pred_proba = self.model.predict_proba(base.X)
|
||||
return _extend_collection(base, pred_proba)
|
||||
return EC.extend_collection(base, pred_proba)
|
||||
|
||||
def fit(self, train: LabelledCollection | ExtendedCollection):
|
||||
@abstractmethod
|
||||
def fit(self, train: LabelledCollection | EC):
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def estimate(self, instances, ext=False):
|
||||
...
|
||||
|
||||
|
||||
class MulticlassAccuracyEstimator(AccuracyEstimator):
|
||||
def __init__(self, c_model: BaseEstimator):
|
||||
self.c_model = c_model
|
||||
self.q_model = SLD(LogisticRegression())
|
||||
self.e_train = None
|
||||
|
||||
def fit(self, train: LabelledCollection | EC):
|
||||
# check if model is fit
|
||||
# self.model.fit(*train.Xy)
|
||||
if isinstance(train, LabelledCollection):
|
||||
pred_prob_train = cross_val_predict(
|
||||
self.model, *train.Xy, method="predict_proba"
|
||||
self.c_model, *train.Xy, method="predict_proba"
|
||||
)
|
||||
|
||||
self.e_train = _extend_collection(train, pred_prob_train)
|
||||
self.e_train = EC.extend_collection(train, pred_prob_train)
|
||||
else:
|
||||
self.e_train = train
|
||||
|
||||
|
|
@ -76,8 +55,8 @@ class AccuracyEstimator:
|
|||
|
||||
def estimate(self, instances, ext=False):
|
||||
if not ext:
|
||||
pred_prob = self.model.predict_proba(instances)
|
||||
e_inst = _extend_instances(instances, pred_prob)
|
||||
pred_prob = self.c_model.predict_proba(instances)
|
||||
e_inst = EC.extend_instances(instances, pred_prob)
|
||||
else:
|
||||
e_inst = instances
|
||||
|
||||
|
|
@ -86,3 +65,51 @@ class AccuracyEstimator:
|
|||
return _check_prevalence_classes(
|
||||
self.e_train.classes_, self.q_model.classes_, estim_prev
|
||||
)
|
||||
|
||||
|
||||
class BinaryQuantifierAccuracyEstimator(AccuracyEstimator):
|
||||
def __init__(self, c_model: BaseEstimator):
|
||||
self.c_model = c_model
|
||||
self.q_model_0 = SLD(LogisticRegression())
|
||||
self.q_model_1 = SLD(LogisticRegression())
|
||||
self.e_train: EC = None
|
||||
|
||||
def fit(self, train: LabelledCollection | EC):
|
||||
# check if model is fit
|
||||
# self.model.fit(*train.Xy)
|
||||
if isinstance(train, LabelledCollection):
|
||||
pred_prob_train = cross_val_predict(
|
||||
self.c_model, *train.Xy, method="predict_proba"
|
||||
)
|
||||
|
||||
self.e_train = EC.extend_collection(train, pred_prob_train)
|
||||
else:
|
||||
self.e_train = train
|
||||
|
||||
[e_train_0, e_train_1] = self.e_train.split_by_pred()
|
||||
|
||||
self.q_model_0.fit(self.e_train_0)
|
||||
self.q_model_1.fit(self.e_train_1)
|
||||
|
||||
def estimate(self, instances, ext=False):
|
||||
# TODO: test
|
||||
if not ext:
|
||||
pred_prob = self.c_model.predict_proba(instances)
|
||||
e_inst = EC.extend_instances(instances, pred_prob)
|
||||
else:
|
||||
e_inst = instances
|
||||
|
||||
_ncl = int(math.sqrt(self.e_train.n_classes))
|
||||
[e_inst_0, e_inst_1] = [
|
||||
e_inst[ind] for ind in EC.split_index_by_pred(_ncl, e_inst)
|
||||
]
|
||||
estim_prev_0 = self.q_model_0.quantify(e_inst_0)
|
||||
estim_prev_1 = self.q_model_1.quantify(e_inst_1)
|
||||
|
||||
estim_prev = []
|
||||
for prev_row in zip(estim_prev_0, estim_prev_1):
|
||||
for prev in prev_row:
|
||||
estim_prev.append(prev)
|
||||
|
||||
return estim_prev
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +1,12 @@
|
|||
import pandas as pd
|
||||
import quapy as qp
|
||||
from quapy.method.aggregative import SLD
|
||||
from quapy.protocol import APP
|
||||
from sklearn.svm import SVC
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
import quacc.evaluation as eval
|
||||
from quacc.estimator import AccuracyEstimator
|
||||
from quacc.estimator import MulticlassAccuracyEstimator
|
||||
|
||||
from .data import get_dataset
|
||||
from quacc.data import get_dataset
|
||||
|
||||
qp.environ["SAMPLE_SIZE"] = 100
|
||||
|
||||
|
|
@ -17,16 +16,17 @@ pd.set_option("display.float_format", "{:.4f}".format)
|
|||
def test_2(dataset_name):
|
||||
train, test = get_dataset(dataset_name)
|
||||
|
||||
model = SVC(probability=True)
|
||||
model = LogisticRegression()
|
||||
|
||||
print(f"fitting model {model.__class__.__name__}...", end=" ", flush=True)
|
||||
model.fit(*train.Xy)
|
||||
print("fit")
|
||||
|
||||
qmodel = SLD(SVC(probability=True))
|
||||
estimator = AccuracyEstimator(model, qmodel)
|
||||
estimator = MulticlassAccuracyEstimator(model)
|
||||
|
||||
print(f"fitting qmodel {qmodel.__class__.__name__}...", end=" ", flush=True)
|
||||
print(
|
||||
f"fitting qmodel {estimator.q_model.__class__.__name__}...", end=" ", flush=True
|
||||
)
|
||||
estimator.fit(train)
|
||||
print("fit")
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,94 @@
|
|||
import pytest
|
||||
from quacc.data import ExClassManager as ECM, ExtendedCollection
|
||||
import numpy as np
|
||||
|
||||
|
||||
class TestExClassManager:
|
||||
@pytest.mark.parametrize(
|
||||
"true_class,pred_class,result",
|
||||
[
|
||||
(0, 0, 0),
|
||||
(0, 1, 1),
|
||||
(1, 0, 2),
|
||||
(1, 1, 3),
|
||||
],
|
||||
)
|
||||
def test_get_ex(self, true_class, pred_class, result):
|
||||
ncl = 2
|
||||
assert ECM.get_ex(ncl, true_class, pred_class) == result
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"ex_class,result",
|
||||
[
|
||||
(0, 0),
|
||||
(1, 1),
|
||||
(2, 0),
|
||||
(3, 1),
|
||||
],
|
||||
)
|
||||
def test_get_pred(self, ex_class, result):
|
||||
ncl = 2
|
||||
assert ECM.get_pred(ncl, ex_class) == result
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"ex_class,result",
|
||||
[
|
||||
(0, 0),
|
||||
(1, 0),
|
||||
(2, 1),
|
||||
(3, 1),
|
||||
],
|
||||
)
|
||||
def test_get_true(self, ex_class, result):
|
||||
ncl = 2
|
||||
assert ECM.get_true(ncl, ex_class) == result
|
||||
|
||||
|
||||
class TestExtendedCollection:
|
||||
@pytest.mark.parametrize(
|
||||
"instances,labels,inst0,lbl0,inst1,lbl1",
|
||||
[
|
||||
(
|
||||
[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]],
|
||||
[3, 0, 1, 2],
|
||||
[[1, 0.54, 0.46], [3, 0.6, 0.4]],
|
||||
[0, 1],
|
||||
[[0, 0.3, 0.7], [2, 0.28, 0.72]],
|
||||
[1, 0],
|
||||
),
|
||||
(
|
||||
[[0, 0.3, 0.7], [2, 0.28, 0.72]],
|
||||
[3, 1],
|
||||
[],
|
||||
[],
|
||||
[[0, 0.3, 0.7], [2, 0.28, 0.72]],
|
||||
[1, 0],
|
||||
),
|
||||
(
|
||||
[[1, 0.54, 0.46], [3, 0.6, 0.4]],
|
||||
[0, 2],
|
||||
[[1, 0.54, 0.46], [3, 0.6, 0.4]],
|
||||
[0, 1],
|
||||
[],
|
||||
[],
|
||||
),
|
||||
|
||||
],
|
||||
)
|
||||
def test_split_by_pred(self, instances, labels, inst0, lbl0, inst1, lbl1):
|
||||
ec = ExtendedCollection(
|
||||
np.asarray(instances), np.asarray(labels), classes=range(0, 4)
|
||||
)
|
||||
[ec0, ec1] = ec.split_by_pred()
|
||||
print(ec0.X, np.asarray(inst0))
|
||||
assert( np.array_equal(ec0.X, np.asarray(inst0)) )
|
||||
print(ec0.y, np.asarray(lbl0))
|
||||
assert( np.array_equal(ec0.y, np.asarray(lbl0)) )
|
||||
print(ec1.X, np.asarray(inst1))
|
||||
assert( np.array_equal(ec1.X, np.asarray(inst1)) )
|
||||
print(ec1.y, np.asarray(lbl1))
|
||||
assert( np.array_equal(ec1.y, np.asarray(lbl1)) )
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
class TestBinaryQuantifierAccuracyEstimator:
|
||||
|
||||
def test_estimate(self):
|
||||
pass
|
||||
Loading…
Reference in New Issue