refactor: Rename src to ibat (#51)

* refactor: Rename src to ibat

* refactor: Rename src to ibat

Makefile

@@ -12,12 +12,12 @@ setup-conda-env:
 
 .PHONY: format
 format:
-	./venv/bin/black ./src
+	./venv/bin/black ./ibat
 	./venv/bin/black ./examples
 
 .PHONY: lint
 lint:
-	./venv/bin/flake8 ./src
+	./venv/bin/flake8 ./ibat
 
 .PHONY: clean
 clean:

examples/concept_drift_detector/adwin.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
-from src.concept_drift_detector import CDD
-from src.concept_drift_detector.strategies import ADWIN
+from ibat.concept_drift_detector import CDD
+from ibat.concept_drift_detector.strategies import ADWIN
 
 
 sorted_mean_arrival_time = pd.read_csv("../../asserts/datasets/input.csv")

examples/concept_drift_detector/ddm.py

@@ -1,8 +1,8 @@
 import pandas as pd
 from xgboost import XGBRegressor
 from sklearn.pipeline import Pipeline
-from src.concept_drift_detector import CDD
-from src.concept_drift_detector.strategies import DDM
+from ibat.concept_drift_detector import CDD
+from ibat.concept_drift_detector.strategies import DDM
 
 
 def split_train_test(split_date, df):

examples/concept_drift_detector/page_hinkley.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
-from src.concept_drift_detector import CDD
-from src.concept_drift_detector.strategies import PageHinkley
+from ibat.concept_drift_detector import CDD
+from ibat.concept_drift_detector.strategies import PageHinkley
 
 
 sorted_mean_arrival_time = pd.read_csv("../../asserts/datasets/input.csv")

examples/pipeline.py

@@ -1,8 +1,8 @@
 from datetime import datetime
 
-from src.concept_drift_detector.strategies import DDM
-from src.datasets import BUS_654_FEATURES_ENCODED_DWELL_TIMES
-from src.pipeline import run_dt_exp
+from ibat.concept_drift_detector.strategies import DDM
+from ibat.datasets import BUS_654_FEATURES_ENCODED_DWELL_TIMES
+from ibat.pipeline import run_dt_exp
 
 
 def datetime_from_string(datetime_string: str) -> datetime:

src/_pipeline.py → ibat/_pipeline.py

@@ -13,12 +13,12 @@
     mean_absolute_percentage_error,
     root_mean_squared_error,
 )
-from src.concept_drift_detector.strategies import IStrategy
-from src.datasets import (
+from ibat.concept_drift_detector.strategies import IStrategy
+from ibat.datasets import (
     BUS_654_FEATURES_ADDED_RUNNING_TIMES,
     BUS_654_FEATURES_ENCODED_DWELL_TIMES,
 )
-from src.models.use_cases.arrival_time.bus import MME4BAT
+from ibat.models.use_cases.arrival_time.bus import MME4BAT
 
 
 def run_exp(

src/concept_drift_detector/concept_drift_detector.py → ibat/concept_drift_detector/concept_drift_detector.py

@@ -1,7 +1,7 @@
 from pandas import DataFrame
-from src.concept_drift_detector.strategies.istrategy import IStrategy
+from ibat.concept_drift_detector.strategies.istrategy import IStrategy
 
-# from src.models.base_models import BaseModel
+# from ibat.models.base_models import BaseModel
 
 
 class CDD:

src/concept_drift_detector/strategies/adwin.py → ibat/concept_drift_detector/strategies/adwin.py

@@ -2,7 +2,7 @@
 
 from river.drift import ADWIN as rADWIN
 from matplotlib import gridspec, pyplot as plt
-from src.concept_drift_detector.strategies.istrategy import IStrategy
+from ibat.concept_drift_detector.strategies.istrategy import IStrategy
 
 
 class ADWIN(IStrategy):

src/concept_drift_detector/strategies/ddm.py → ibat/concept_drift_detector/strategies/ddm.py

@@ -4,9 +4,9 @@
 from sklearn.metrics import accuracy_score, mean_absolute_percentage_error, r2_score
 from frouros.detectors.concept_drift import DDM as fDDM, DDMConfig
 from frouros.metrics import PrequentialError
-from src.concept_drift_detector.strategies.istrategy import IStrategy
+from ibat.concept_drift_detector.strategies.istrategy import IStrategy
 
-# from src.models.base_models import BaseModel
+# from ibat.models.base_models import BaseModel
 
 
 class DDM(IStrategy):

src/concept_drift_detector/strategies/istrategy.py → ibat/concept_drift_detector/strategies/istrategy.py

@@ -3,7 +3,7 @@
 
 from pandas import DataFrame
 
-# from src.models.base_models.base_models import BaseModel
+# from ibat.models.base_models.base_models import BaseModel
 
 
 class IStrategy:

src/concept_drift_detector/strategies/page_hinkley.py → ibat/concept_drift_detector/strategies/page_hinkley.py

@@ -2,7 +2,7 @@
 
 from river.drift import PageHinkley as rPageHinkley
 from matplotlib import gridspec, pyplot as plt
-from src.concept_drift_detector.strategies.istrategy import IStrategy
+from ibat.concept_drift_detector.strategies.istrategy import IStrategy
 
 
 class PageHinkley(IStrategy):

src/models/base_models/base_models.py → ibat/models/base_models/base_models.py

@@ -5,7 +5,7 @@
 from pandas import concat, DataFrame, Series
 from xgboost import Booster, DMatrix, train
 from river.stream import iter_pandas
-from src.models.base_models.ibase_model import IBaseModel
+from ibat.models.base_models.ibase_model import IBaseModel
 
 
 class BaseModel(IBaseModel, ABC):

src/models/base_models/ensemble/adaboost.py → ibat/models/base_models/ensemble/adaboost.py

@@ -1,6 +1,6 @@
 from river.tree import HoeffdingTreeClassifier
 from river.ensemble import AdaBoostClassifier as ExAdaBoostClassifier
-from src.models.base_models.base_models import RiverStreamBaseModel
+from ibat.models.base_models.base_models import RiverStreamBaseModel
 
 
 class AdaBoostClassifier(RiverStreamBaseModel):

src/models/base_models/ensemble/decision_tree.py → ibat/models/base_models/ensemble/decision_tree.py

@@ -1,5 +1,5 @@
 from river.tree import ExtremelyFastDecisionTreeClassifier
-from src.models.base_models.base_models import RiverStreamBaseModel
+from ibat.models.base_models.base_models import RiverStreamBaseModel
 
 
 class DecisionTreeClassifier(RiverStreamBaseModel):

src/models/base_models/ensemble/hoeffding_tree.py → ibat/models/base_models/ensemble/hoeffding_tree.py

@@ -4,7 +4,7 @@
     HoeffdingTreeClassifier as ExHoeffdingTreeClassifier,
     HoeffdingTreeRegressor as ExHoeffdingTreeRegressor,
 )
-from src.models.base_models.base_models import RiverStreamBaseModel
+from ibat.models.base_models.base_models import RiverStreamBaseModel
 
 """
 Tree-based models are popular due to their interpretability. Hoeffding Tree uses a tree data structure to model

src/models/base_models/ensemble/random_forest.py → ibat/models/base_models/ensemble/random_forest.py

@@ -1,6 +1,6 @@
 # from river.forest import (ARFClassifier as ExARFClassifier, ARFRegressor as ExARFRegressor)
 # from river.preprocessing import StandardScaler
-# from src.models.base_models.base_models import RiverStreamBaseModel
+# from ibat.models.base_models.base_models import RiverStreamBaseModel
 #
 # """
 # The 3 most important aspects of ARF are:

src/models/base_models/ensemble/sgt.py → ibat/models/base_models/ensemble/sgt.py

@@ -1,6 +1,6 @@
 from river.tree import SGTRegressor as ExSGTRegressor
 from river.tree.splitter import DynamicQuantizer
-from src.models.base_models.base_models import RiverStreamBaseModel
+from ibat.models.base_models.base_models import RiverStreamBaseModel
 
 
 class SGTRegressor(RiverStreamBaseModel):

src/models/base_models/ensemble/streaming_random_patches.py → ibat/models/base_models/ensemble/streaming_random_patches.py

@@ -1,6 +1,6 @@
 from river.ensemble import SRPClassifier as ExSRPClassifier
 from river.tree import HoeffdingTreeClassifier
-from src.models.base_models.base_models import RiverStreamBaseModel
+from ibat.models.base_models.base_models import RiverStreamBaseModel
 
 """
 SRP is an ensemble method that simulates bagging or random subspaces.

src/models/base_models/ensemble/xgboost.py → ibat/models/base_models/ensemble/xgboost.py

@@ -1,4 +1,4 @@
-from src.models.base_models.base_models import XGBoost
+from ibat.models.base_models.base_models import XGBoost
 
 
 class XGBClassifier(XGBoost):

src/models/base_models/linear_model/linear_river_many.py → ibat/models/base_models/linear_model/linear_river_many.py

@@ -4,7 +4,7 @@
 )
 from river.optim import SGD
 from river.preprocessing import StandardScaler
-from src.models.base_models.base_models import RiverBatchBaseModel
+from ibat.models.base_models.base_models import RiverBatchBaseModel
 
 
 class LinearRegression(RiverBatchBaseModel):

src/models/base_models/linear_model/passive_aggressive.py → ibat/models/base_models/linear_model/passive_aggressive.py

@@ -2,7 +2,7 @@
     PassiveAggressiveClassifier as ExPassiveAggressiveClassifier,
     PassiveAggressiveRegressor as ExPassiveAggressiveRegressor,
 )
-from src.models.base_models.base_models import SKLearnBaseModel
+from ibat.models.base_models.base_models import SKLearnBaseModel
 
 
 class PassiveAggressiveClassifier(SKLearnBaseModel):

src/models/base_models/linear_model/perceptron.py → ibat/models/base_models/linear_model/perceptron.py

@@ -1,5 +1,5 @@
 from sklearn.linear_model import Perceptron
-from src.models.base_models.base_models import SKLearnBaseModel
+from ibat.models.base_models.base_models import SKLearnBaseModel
 
 
 class PerceptronClassifier(SKLearnBaseModel):

src/models/base_models/linear_model/sgd.py → ibat/models/base_models/linear_model/sgd.py

@@ -2,7 +2,7 @@
     SGDClassifier as ExSGDClassifier,
     SGDRegressor as ExSGDRegressor,
 )
-from src.models.base_models.base_models import SKLearnBaseModel
+from ibat.models.base_models.base_models import SKLearnBaseModel
 
 
 class SGDClassifier(SKLearnBaseModel):

src/models/base_models/naive_bayes/naive_bayes.py → ibat/models/base_models/naive_bayes/naive_bayes.py

@@ -2,7 +2,7 @@
     BernoulliNB as ExBernoulliNB,
     MultinomialNB as ExMultinomialNB,
 )
-from src.models.base_models.base_models import SKLearnBaseModel
+from ibat.models.base_models.base_models import SKLearnBaseModel
 
 
 class BernoulliNB(SKLearnBaseModel):

src/models/base_models/naive_bayes/naive_bayes_river.py → ibat/models/base_models/naive_bayes/naive_bayes_river.py

@@ -3,7 +3,7 @@
     BernoulliNB as ExBernoulliNB,
     MultinomialNB as ExMultinomialNB,
 )
-from src.models.base_models.base_models import RiverBatchBaseModel
+from ibat.models.base_models.base_models import RiverBatchBaseModel
 
 
 class BernoulliNB(RiverBatchBaseModel):

src/models/base_models/nn/mlp.py → ibat/models/base_models/nn/mlp.py

@@ -2,7 +2,7 @@
     MLPClassifier as ExMLPClassifier,
     MLPRegressor as ExMLPRegressor,
 )
-from src.models.base_models.base_models import SKLearnBaseModel
+from ibat.models.base_models.base_models import SKLearnBaseModel
 
 
 class MLPClassifier(SKLearnBaseModel):

src/models/base_models/nn/mlp_river.py → ibat/models/base_models/nn/mlp_river.py

@@ -2,7 +2,7 @@
 from river.neural_net.activations import ReLU
 from river.optim import SGD
 from river.preprocessing import StandardScaler
-from src.models.base_models.base_models import RiverBatchBaseModel
+from ibat.models.base_models.base_models import RiverBatchBaseModel
 
 
 class MLPRegressor(RiverBatchBaseModel):

src/models/use_cases/arrival_time/bus/mme4bat.py → ibat/models/use_cases/arrival_time/bus/mme4bat.py

@@ -1,9 +1,9 @@
 from copy import deepcopy
 from typing import Optional
 
-from src.concept_drift_detector import CDD
-from src.concept_drift_detector.strategies import IStrategy
-from src.models.base_models.ensemble.xgboost import XGBClassifier, XGBRegressor
+from ibat.concept_drift_detector import CDD
+from ibat.concept_drift_detector.strategies import IStrategy
+from ibat.models.base_models.ensemble.xgboost import XGBClassifier, XGBRegressor
 
 
 class MME4BAT:

src/models/use_cases/dwell_time/bus/mme4bdt.py → ibat/models/use_cases/dwell_time/bus/mme4bdt.py

@@ -1,9 +1,9 @@
 from copy import deepcopy
 from typing import Optional
 
-from src.concept_drift_detector import CDD
-from src.concept_drift_detector.strategies import IStrategy
-from src.models.base_models.ensemble.xgboost import XGBClassifier, XGBRegressor
+from ibat.concept_drift_detector import CDD
+from ibat.concept_drift_detector.strategies import IStrategy
+from ibat.models.base_models.ensemble.xgboost import XGBClassifier, XGBRegressor
 
 
 class MME4BDT:

src/pipeline.py → ibat/pipeline.py

@@ -11,8 +11,8 @@
     mean_absolute_error,
     root_mean_squared_error,
 )
-from src.concept_drift_detector.strategies import IStrategy
-from src.models.use_cases.dwell_time.bus import MME4BDT
+from ibat.concept_drift_detector.strategies import IStrategy
+from ibat.models.use_cases.dwell_time.bus import MME4BDT
 
 
 def run_dt_exp(

setup.py

@@ -1,14 +1,14 @@
-from setuptools import setup, find_packages
+from setuptools import find_packages, setup
 from codecs import open
 from os import path
 
 
 HERE = path.abspath(path.dirname(__file__))
 
-with open(path.join(HERE, "README.md"), "r", encoding='utf-8') as f:
+with open(path.join(HERE, "README.md"), "r", encoding="utf-8") as f:
     long_description = f.read()
 
-with open(path.join(HERE, "requirements.txt"), "r", encoding='utf-8') as f:
+with open(path.join(HERE, "requirements.txt"), "r", encoding="utf-8") as f:
     requirements = f.read().splitlines()
 
 classifiers = [
@@ -40,19 +40,17 @@
         "Incremental learning",
     ],
     author="Aaivu",
-    author_email='[email protected]',
+    author_email="[email protected]",
     license="MIT",
     python_requires=">=3.9",
     classifiers=classifiers,
-    packages=find_packages(where="src"),
-    package_dir={"": "src"},
+    packages=find_packages(exclude=["tests", "tests.*"]),
     include_package_data=True,
     install_requires=requirements,
     project_urls={
-        "Homepage": "https://github.com/aaivu/ibat",
         "Source Code": "https://github.com/aaivu/ibat",
         "Download": "https://github.com/aaivu/ibat/releases",
         "Documentation": "https://github.com/aaivu/ibat/blob/master/README.md",
         "Bug Tracker": "https://github.com/aaivu/ibat/issues",
-    }
+    },
 )