fix issues in logging, bug in space.py, constraint sign, and improve code coverage (#388)

* console log handler * version update * doc * skippable steps * notebook update * constraint sign * doc for constraints * bug fix: define-by-run and unflatten_hierarchical * const * handle nested space in indexof() * test grid search * test suggestion * model test * >1 ckpts * always increase iter count * log total # iterations * security patch * make iter_per_learner consistent
2026-04-20 03:02:16 -04:00 · 2022-01-14 13:39:09 -08:00
parent c1b5cb5348
commit 569908fbe6
25 changed files with 885 additions and 492 deletions
--- a/test/test_model.py
+++ b/test/test_model.py
@@ -0,0 +1,117 @@
+from sklearn.datasets import make_classification
+import numpy as np
+from pandas import DataFrame
+from datetime import datetime
+from flaml.model import (
+    KNeighborsEstimator,
+    LRL2Classifier,
+    BaseEstimator,
+    LGBMEstimator,
+    CatBoostEstimator,
+    XGBoostEstimator,
+    RandomForestEstimator,
+    Prophet,
+    ARIMA,
+    LGBM_TS_Regressor,
+)
+
+
+def test_lrl2():
+    BaseEstimator.search_space(1, "")
+    X, y = make_classification(100000, 1000)
+    print("start")
+    lr = LRL2Classifier()
+    lr.predict(X)
+    lr.fit(X, y, budget=1e-5)
+
+
+def test_prep():
+    X = np.array(
+        list(
+            zip(
+                [
+                    3.0,
+                    16.0,
+                    10.0,
+                    12.0,
+                    3.0,
+                    14.0,
+                    11.0,
+                    12.0,
+                    5.0,
+                    14.0,
+                    20.0,
+                    16.0,
+                    15.0,
+                    11.0,
+                ],
+                [
+                    "a",
+                    "b",
+                    "a",
+                    "c",
+                    "c",
+                    "b",
+                    "b",
+                    "b",
+                    "b",
+                    "a",
+                    "b",
+                    1.0,
+                    1.0,
+                    "a",
+                ],
+            )
+        ),
+        dtype=object,
+    )
+    y = np.array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
+    lr = LRL2Classifier()
+    lr.fit(X, y)
+    lr.predict(X)
+    lgbm = LGBMEstimator(n_estimators=4)
+    lgbm.fit(X, y)
+    cat = CatBoostEstimator(n_estimators=4)
+    cat.fit(X, y)
+    knn = KNeighborsEstimator(task="regression")
+    knn.fit(X, y)
+    xgb = XGBoostEstimator(n_estimators=4, max_leaves=4)
+    xgb.fit(X, y)
+    xgb.predict(X)
+    rf = RandomForestEstimator(task="regression", n_estimators=4, criterion="gini")
+    rf.fit(X, y)
+
+    prophet = Prophet()
+    try:
+        prophet.predict(4)
+    except ValueError:
+        # predict() with steps is only supported for arima/sarimax.
+        pass
+    prophet.predict(X)
+
+    arima = ARIMA()
+    arima.predict(X)
+    arima._model = False
+    try:
+        arima.predict(X)
+    except ValueError:
+        # X_test needs to be either a pandas Dataframe with dates as the first column or an int number of periods for predict().
+        pass
+
+    lgbm = LGBM_TS_Regressor(optimize_for_horizon=True, lags=1)
+    X = DataFrame(
+        {
+            "A": [
+                datetime(1900, 2, 3),
+                datetime(1900, 3, 4),
+                datetime(1900, 3, 4),
+                datetime(1900, 3, 4),
+                datetime(1900, 7, 2),
+                datetime(1900, 8, 9),
+            ],
+        }
+    )
+    y = np.array([0, 1, 0, 1, 0, 0])
+    lgbm.predict(X[:2])
+    lgbm.fit(X, y, period=2)
+    lgbm.predict(X[:2])