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Feature(Speed): Python launches faster by conditionally importing models. (#169)

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* feat: Added optional import of models.

* fix: Models weren't wrapped into abstract class, fixed it.

* chore: Deleted leftover comments.

* fix: Same merge commit as on remote.

* fix: System wasn't putting in RF because there was no differentiation between RF as regressor and RF as classificator.

* fix(Models): use the XGBoostModel wrapper

Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
This commit is contained in:
Daniel Szemerey
2022-01-14 14:29:24 +01:00
committed by GitHub
parent 797d45d036
commit 31dc847be1
8 changed files with 119 additions and 77 deletions
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config/config.py
+2 -2
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@@ -72,8 +72,8 @@ def get_default_ensemble_config() -> tuple[dict, dict, dict]:
narrow_format = False,
)
regression_models = ["Lasso", "KNN", "RF"]
classification_models = ["LR_two_class", "LDA", "NB", "RF", "XGB_two_class", "LGBM", "StaticMom"]
regression_models = ["Lasso", "KNN", "RFR"]
classification_models = ["LR_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
meta_labeling_models = ['LR_two_class', 'LGBM']
ensemble_model = 'Average'
config/preprocess.py
+2 -1
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@@ -1,7 +1,7 @@
from utils.helpers import flatten
from feature_extractors.feature_extractor_presets import presets as feature_extractor_presets
from models.model_map import model_map
from models.model_map import get_model_map
from data_loader.collections import data_collections
def preprocess_config(model_config:dict, training_config:dict, data_config:dict) -> tuple[dict, dict, dict]:
@@ -21,6 +21,7 @@ def __preprocess_feature_extractors_config(data_dict: dict) -> dict:
return data_dict
def __preprocess_model_config(model_config:dict, method:str) -> dict:
model_map, _, _, _, _ = get_model_map(model_config)
model_config['primary_models'] = [(model_name, model_map[method + '_models'][model_name]) for model_name in model_config['primary_models']]
if len(model_config['meta_labeling_models']) > 0:
model_config['meta_labeling_models'] = [(model_name, model_map[method + '_models'][model_name]) for model_name in model_config['meta_labeling_models']]
data_loader/process_data.py
+3 -1
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@@ -3,16 +3,18 @@ from operator import itemgetter
from utils.helpers import has_enough_samples_to_train
from feature_selection.dim_reduction import reduce_dimensionality
from models.model_map import default_feature_selector_regression, default_feature_selector_classification
from models.model_map import get_model_map
from feature_selection.feature_selection import select_features
import warnings
def process_data(X:pd.DataFrame, y:pd.Series, configs: dict) -> tuple[pd.DataFrame,pd.DataFrame]:
model_config, training_config, data_config = itemgetter('model_config', 'training_config', 'data_config')(configs)
_, _, _, default_feature_selector_regression, default_feature_selector_classification = get_model_map(model_config)
original_X = X.copy()
models/model_map.py
+104 -65
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@@ -1,72 +1,111 @@
from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, Ridge
from sklearn.linear_model import LogisticRegression
from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
from sklearn.tree import DecisionTreeClassifier
from sklearnex.neighbors import KNeighborsRegressor, KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearnex.svm import SVR, SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor, ExtraTreesRegressor, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearnex.ensemble import RandomForestClassifier
from models.sklearn import SKLearnModel
from models.neural import LightningNeuralNetModel
from models.momentum import StaticMomentumModel
from models.average import StaticAverageModel
from models.naive import StaticNaiveModel
from models.pytorch.neural_nets import MultiLayerPerceptron
from models.xgboost import XGBoostModel
from models.statsmodels import StatsModel
from xgboost import XGBClassifier
import torch.nn.functional as F
from lightgbm import LGBMClassifier
from statsmodels.tsa.api import ExponentialSmoothing
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
model_map = {
"regression_models": dict(
LR= SKLearnModel(LinearRegression(n_jobs=-1)),
Lasso= SKLearnModel(Lasso(alpha=100, random_state=1)),
Ridge= SKLearnModel(Ridge(alpha=0.1)),
BayesianRidge= SKLearnModel(BayesianRidge()),
KNN= SKLearnModel(KNeighborsRegressor(n_neighbors=25)),
AB= SKLearnModel(AdaBoostRegressor(random_state=1)),
MLP= SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
RF= SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1)),
SVR= SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1)),
StaticNaive= StaticNaiveModel(),
DNN = LightningNeuralNetModel(
MultiLayerPerceptron(
hidden_layers_ratio = [1.0],
probabilities = False,
loss_function = F.mse_loss),
max_epochs=15
)
),
"classification_models": dict(
LR_two_class= SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000)),
LR_three_class= SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1)),
LDA= SKLearnModel(LinearDiscriminantAnalysis()),
KNN= SKLearnModel(KNeighborsClassifier()),
CART= SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1)),
NB= SKLearnModel(GaussianNB()),
AB= SKLearnModel(AdaBoostClassifier(n_estimators=15)),
RF= SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)),
SVC = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1)),
XGB_two_class= XGBoostModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', use_label_encoder= False, eval_metric='mlogloss')),
LGBM = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1)),
StaticMom= StaticMomentumModel(allow_short=True),
# ExpSmoothing = SKLearnModel(ExponentialSmoothing(trend='add', seasonal='add', seasonal_periods=30)),
),
"ensemble_models": dict(
Average= StaticAverageModel(),
)
"regression_models": dict(),
"classification_models": dict(),
"ensemble_models": dict()
}
model_names_classification = list(model_map["classification_models"].keys())
model_names_regression = list(model_map["regression_models"].keys())
def get_model_map(config:dict):
if len(config['primary_models']) > 0 and isinstance(config['primary_models'][0], str):
print("Going to Load models")
combined_list = config['primary_models'] + config['meta_labeling_models'] + [config['ensemble_model']]
for model_name in combined_list:
if model_name == 'LR':
from sklearn.linear_model import LinearRegression
model_map['regression_models']['LR'] = SKLearnModel(LinearRegression(n_jobs=-1))
elif model_name == 'Lasso':
from sklearn.linear_model import Lasso
model_map['regression_models']['Lasso'] = SKLearnModel(Lasso(alpha=100, random_state=1))
elif model_name == 'Ridge':
from sklearn.linear_model import Ridge
model_map['regression_models']['Ridge'] = SKLearnModel(Ridge(alpha=0.1))
elif model_name == 'BayesianRidge':
from sklearn.linear_model import BayesianRidge
model_map['regression_models']['BayesianRidge'] = SKLearnModel(BayesianRidge())
elif model_name == 'KNN':
from sklearnex.neighbors import KNeighborsRegressor
model_map['regression_models']['KNN'] = SKLearnModel(KNeighborsRegressor(n_neighbors=25))
elif model_name == 'AB':
from sklearn.ensemble import AdaBoostRegressor
model_map['regression_models']['AB'] = SKLearnModel(AdaBoostRegressor(random_state=1))
elif model_name == 'MLP':
from sklearn.neural_network import MLPRegressor
model_map['regression_models']['MLP'] = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000))
elif model_name == 'RFR':
# from sklearn.ensemble import RandomForestRegressor
model_map['regression_models']['RFR'] = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1))
elif model_name == 'SVR':
from sklearnex.svm import SVR
model_map['regression_models']['SVR'] = SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1))
elif model_name == 'StaticNaive':
from models.naive import StaticNaiveModel
model_map['regression_models']['StaticNaive'] = StaticNaiveModel()
elif model_name == 'DNN':
from models.neural import LightningNeuralNetModel
from models.pytorch.neural_nets import MultiLayerPerceptron
import torch.nn.functional as F
model_map['regression_models']['DNN'] = LightningNeuralNetModel(
MultiLayerPerceptron(
hidden_layers_ratio = [1.0],
probabilities = False,
loss_function = F.mse_loss),
max_epochs=15
)
elif model_name == 'LR_two_class':
from sklearn.linear_model import LogisticRegression
model_map['classification_models']['LR_two_class'] = SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000))
elif model_name == 'LR_three_class':
from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
model_map['classification_models']['LR_three_class'] = SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1))
elif model_name == 'LDA':
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
model_map['classification_models']['LDA'] = SKLearnModel(LinearDiscriminantAnalysis())
elif model_name == 'KNN':
from sklearn.neighbors import KNeighborsClassifier
model_map['classification_models']['KNN'] = SKLearnModel(KNeighborsClassifier())
elif model_name == 'CART':
from sklearn.tree import DecisionTreeClassifier
model_map['classification_models']['CART'] = SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1))
elif model_name == 'NB':
from sklearn.naive_bayes import GaussianNB
model_map['classification_models']['NB'] = SKLearnModel(GaussianNB())
elif model_name == 'AB':
from sklearn.ensemble import AdaBoostClassifier
model_map['classification_models']['AB'] = SKLearnModel(AdaBoostClassifier(n_estimators=15))
elif model_name == 'RFC':
# from sklearn.ensemble import RandomForestClassifier
model_map['classification_models']['RFC'] = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1))
elif model_name == 'SVC':
from sklearn.svm import SVC
model_map['classification_models']['SVC'] = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1))
elif model_name == 'XGB_two_class':
from xgboost import XGBClassifier
from models.xgboost import XGBoostModel
model_map['classification_models']['XGB_two_class'] = XGBoostModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', use_label_encoder= False, eval_metric='mlogloss'))
elif model_name == 'LGBM':
from lightgbm import LGBMClassifier
model_map['classification_models']['LGBM'] = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1))
elif model_name == 'StaticMom':
from models.momentum import StaticMomentumModel
model_map['classification_models']['StaticMom'] = StaticMomentumModel(allow_short=True)
elif model_name == 'Average':
from models.average import StaticAverageModel
model_map['ensemble_models']['Average'] = StaticAverageModel()
model_names_classification = list(model_map["classification_models"].keys())
model_names_regression = list(model_map["regression_models"].keys())
default_feature_selector_regression = model_map['regression_models']['RF']
default_feature_selector_classification = model_map['classification_models']['RF']
default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1))
default_feature_selector_regression = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1))
return model_map, model_names_classification, model_names_regression, default_feature_selector_regression, default_feature_selector_classification
sweep_ensemble.yaml
+3 -3
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@@ -47,9 +47,9 @@ parameters:
primary_models:
distribution: categorical
values:
- ["LDA", "LR_two_class", "KNN", "SVC", "CART", "NB", "AB", "RF", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "NB", "RF", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "LGBM", "RF", "XGB_two_class"]
- ["LDA", "LR_two_class", "KNN", "SVC", "CART", "NB", "AB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
- ["LR_two_class", "LDA", "LGBM", "RFC", "XGB_two_class"]
meta_labeling_models:
value: ["LGBM", "LR_two_class"]
own_features:
sweep_meta_labeling.yaml
+2 -2
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@@ -51,9 +51,9 @@ parameters:
index_column:
value: 'int'
primary_models:
value: ["LR_two_class", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
value: ["LR_two_class", "LDA", "KNN", "CART", "NB", "AB", "RFC", "StaticMom"]
meta_labeling_models:
values: ["LR_two_class", "LDA", "KNN", "CART", "NB", "AB", "RF"]
values: ["LR_two_class", "LDA", "KNN", "CART", "NB", "AB", "RFC"]
distribution: categorical
own_features:
values: [['single_mom', 'date_days'], [], ['level_1', 'date_days'], ['date_days', 'level_2']]
sweep_primary_models.yaml
+1 -1
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@@ -45,7 +45,7 @@ parameters:
index_column:
value: 'int'
primary_models:
values: [['LR_two_class'], ['SVC'], ['LDA'], ['KNN'], ['CART'], ['MNB'], ['NB'], ['AB'], ['RF'], ['XGB_two_class'], ['LGBM']]
values: [['LR_two_class'], ['SVC'], ['LDA'], ['KNN'], ['CART'], ['MNB'], ['NB'], ['AB'], ['RFC'], ['XGB_two_class'], ['LGBM']]
distribution: categorical
meta_labeling_models:
value: ["LGBM", "LR_two_class"]
training/meta_labeling.py
+2 -2
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@@ -3,7 +3,7 @@ from utils.helpers import random_string, equal_except_nan, drop_until_first_vali
from training.primary_model import train_primary_model
from feature_selection.feature_selection import select_features
import pandas as pd
from models.model_map import default_feature_selector_regression, default_feature_selector_classification
from models.model_map import get_model_map
from models.base import Model
from reporting.types import Reporting
from typing import Union
@@ -23,7 +23,7 @@ def train_meta_labeling_model(
preloaded_models: Union[list[Reporting.Single_Model], None] = None
) -> tuple[pd.Series, pd.Series, pd.DataFrame, list[Reporting.Single_Model]]:
_, _, _, default_feature_selector_regression, default_feature_selector_classification = get_model_map(model_config)
discretize = discretize_threeway_threshold(0.33)
discretized_predictions = input_predictions.apply(discretize)
meta_y: pd.Series = pd.concat([discretized_predictions, y], axis=1).apply(equal_except_nan, axis = 1)