feat(Models): added optional hpsklearn (#236)

environment.yml

@@ -17,22 +17,23 @@ dependencies:
   - ipython
   - ipykernel
   - scipy
+  - tqdm
+  - numpy
+  - pandas
+  - matplotlib
+  - quantstats
+  - pytest
+  - wandb
+  - pandas-ta
   - pip
   - pip:
     - black
     - fracdiff
     - diskcache
-    - matplotlib
-    - numpy
-    - quantstats
-    - pytest
-    - wandb
     - python-dotenv
-    - tscv
-    - tqdm
-    - pandas-ta
-    - alphalens-reloaded
     - vectorbt
     - pandera[mypy]
     - binance_historical_data
+    - alphalens-reloaded
+
 prefix: /usr/local/anaconda3/envs/quant

models/model_map.py

@@ -1,5 +1,5 @@
 from models.sklearn import SKLearnModel
-from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.ensemble import RandomForestClassifier
 from .base import Model
 
 default_feature_selector_classification = SKLearnModel(
@@ -75,6 +75,19 @@ def get_model(model_name: str) -> Model:
             SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1))
         )
 
+    elif model_name == "HyperOpt":
+        from hpsklearn import HyperoptEstimator
+        from hyperopt import tpe
+
+        return set_name(
+            SKLearnModel(
+                HyperoptEstimator(
+                    algo=tpe.suggest,
+                    trial_timeout=300,
+                )
+            )
+        )
+
     elif model_name == "AutoML":
         from supervised.automl import AutoML