Feature: Added sweep functionality (#65)

* feat: Parametricized model selection works now. * feat: Fixed errors. Sweep generates and you can run it, but it gives an error for model.only_columns attribute. * feat: Factored the wandb management, default config managment and the model_dictionary out of the run_pipeline to a seperate file. * fix: Took out prints and fixed the mismatch of ensemble models when classifing. * fix(Models): added StaticMomentum model to the dictionary, hopefully fixed sklearn-ex RandomForestRegressor problem * fix(Dependencies): pin scikit-learn-ex's version, moved map_model_name_to_function to `models` * feat(Sweep): added `run_sweep.py` shortcut * feat(Pipeline): skip training a meta model if array is empty Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
2026-07-27 18:57:55 +00:00 · 2021-12-21 17:28:36 +01:00
parent d3d7184ea4
commit 1c1b8b2e54
8 changed files with 222 additions and 151 deletions
@@ -0,0 +1,58 @@
+from sklearn.linear_model import LinearRegression, Lasso, BayesianRidge, LogisticRegression, Ridge
+from sklearn.tree import DecisionTreeClassifier
+from sklearnex.neighbors import KNeighborsRegressor, KNeighborsClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearnex.svm import SVR
+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.base import SKLearnModel
+from models.momentum import StaticMomentumModel
+from models.average import StaticAverageModel
+from models.naive import StaticNaiveModel
+
+
+model_map = {
+    "regression_models": dict(
+        Lasso = SKLearnModel(Lasso(alpha=0.1, max_iter=1000)),
+        Ridge = SKLearnModel(Ridge(alpha=0.1)),
+        BayesianRidge = SKLearnModel(BayesianRidge()),
+        KNN = SKLearnModel(KNeighborsRegressor(n_neighbors=25)),
+        AB = SKLearnModel(AdaBoostRegressor(random_state=1)),
+        LR = SKLearnModel(LinearRegression(n_jobs=-1)),
+        MLP = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000)),
+        RF = SKLearnModel(RandomForestRegressor(n_jobs=-1)),
+        SVR = SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1)),
+        StaticNaive = StaticNaiveModel(),
+    ),
+    "classification_models": dict(
+        LR= SKLearnModel(LogisticRegression(n_jobs=-1)),
+        LDA= SKLearnModel(LinearDiscriminantAnalysis()),
+        KNN= SKLearnModel(KNeighborsClassifier()),
+        CART= SKLearnModel(DecisionTreeClassifier()),
+        NB= SKLearnModel(GaussianNB()),
+        AB= SKLearnModel(AdaBoostClassifier()),
+        RF= SKLearnModel(RandomForestClassifier(n_jobs=-1)),
+        StaticMom= StaticMomentumModel(allow_short=True),
+    ),     
+    "classification_ensemble_models": dict(
+        Ensemble_CART = SKLearnModel(DecisionTreeClassifier()),
+        Ensemble_Average = StaticAverageModel(),
+    ),
+    "regression_ensemble_models": dict(
+        Ensemble_Ridge = SKLearnModel(Ridge(alpha=0.1)),
+        Ensemble_Average = StaticAverageModel(),
+    )
+}
+
+model_names_classification = model_map["classification_models"].keys()
+model_names_regression = model_map["regression_models"].keys()
+
+
+def map_model_name_to_function(model_config:dict, method:str) -> dict:
+    for level in ['level_1_models', 'level_2_models']:
+        model_category = method + '_models' if level=='level_1_models' else method + '_ensemble_models'
+        model_config[level] = [(model_name, model_map[model_category][model_name]) for model_name in  model_config[level]]
+
+    return model_config