reintegrate prophet in core deps (#1054)

* reintegrate prophet in core deps * less strict requirement on ipython * rm pmdarima flavour; rm conditional tests * remove flavours from build.gradle * remove flavours from build.gradle * remove pmdarima from setup.py
unit8co · Jul 12, 2022 · 7ffa3fc · 7ffa3fc
1 parent 12c3702
commit 7ffa3fc
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Showing 11 changed files with 243 additions and 297 deletions.
diff --git a/build.gradle b/build.gradle
@@ -80,7 +80,7 @@ void createPipInstallTask(String flavour) {
    }
 }
 
-String[] flavours = ["core", "prophet", "pmdarima", "dev", "torch", "release"];
+String[] flavours = ["core", "dev", "torch", "release"];
 
 for(String flavour : flavours) {
     createPipInstallTask(flavour);
@@ -94,7 +94,7 @@ task pipInstall() {
     doFirst {
         setupPip
     }
-    dependsOn pip_core, pip_dev, pip_pmdarima, pip_torch, pip_release
+    dependsOn pip_core, pip_dev, pip_torch, pip_release
 }
 
 task lint_black(type: Exec) {
@@ -134,7 +134,7 @@ void createPipRelatedTask(String flavour) {
    }
 }
 
-flavours = ["core", "prophet", "pmdarima", "torch"];
+flavours = ["core", "torch"];
 
 for(String flavour : flavours) {
     createPipRelatedTask(flavour);

diff --git a/darts/models/__init__.py b/darts/models/__init__.py
@@ -9,41 +9,23 @@
 
 # Forecasting
 from darts.models.forecasting.arima import ARIMA
+from darts.models.forecasting.auto_arima import AutoARIMA
 from darts.models.forecasting.baselines import NaiveDrift, NaiveMean, NaiveSeasonal
 from darts.models.forecasting.catboost_model import CatBoostModel
 from darts.models.forecasting.croston import Croston
 from darts.models.forecasting.exponential_smoothing import ExponentialSmoothing
 from darts.models.forecasting.fft import FFT
 from darts.models.forecasting.kalman_forecaster import KalmanForecaster
 from darts.models.forecasting.linear_regression_model import LinearRegressionModel
+from darts.models.forecasting.prophet_model import Prophet
 from darts.models.forecasting.random_forest import RandomForest
 from darts.models.forecasting.regression_ensemble_model import RegressionEnsembleModel
 from darts.models.forecasting.regression_model import RegressionModel
 from darts.models.forecasting.sf_auto_arima import StatsForecastAutoARIMA
+from darts.models.forecasting.tbats import BATS, TBATS
 from darts.models.forecasting.theta import FourTheta, Theta
 from darts.models.forecasting.varima import VARIMA
 
-try:
-    from darts.models.forecasting.auto_arima import AutoARIMA
-    from darts.models.forecasting.tbats import BATS, TBATS
-except ModuleNotFoundError:
-    logger.warning(
-        "Support for AutoARIMA, BATS and TBATS is not available."
-        "To enable it, install u8darts[pmdarima] or u8darts[all]."
-    )
-
-try:
-    from darts.models.forecasting.prophet_model import Prophet
-except ModuleNotFoundError:
-    pass
-    """
-    logger.warning(
-        "Support for Facebook Prophet is not available. "
-        "To enable it, install the prophet package in your environment; see "
-        "https://facebook.github.io/prophet/docs/installation.html"
-    )
-    """
-
 try:
     from darts.models.forecasting.block_rnn_model import BlockRNNModel
     from darts.models.forecasting.nbeats import NBEATSModel

diff --git a/darts/tests/models/forecasting/test_local_forecasting_models.py b/darts/tests/models/forecasting/test_local_forecasting_models.py
@@ -6,13 +6,17 @@
 from darts.metrics import mape
 from darts.models import (
     ARIMA,
+    BATS,
     FFT,
+    TBATS,
     VARIMA,
+    AutoARIMA,
     Croston,
     ExponentialSmoothing,
     FourTheta,
     KalmanForecaster,
     NaiveSeasonal,
+    Prophet,
     StatsForecastAutoARIMA,
     Theta,
 )
@@ -70,25 +74,14 @@
 dual_models = [ARIMA(), StatsForecastAutoARIMA(period=12)]
 
 
-try:
-    from darts.models import Prophet
-
-    models.append((Prophet(), 13.5))
-    dual_models.append(Prophet())
-except ImportError:
-    logger.warning("Prophet not installed - will be skipping Prophet tests")
+models.append((Prophet(), 13.5))
+dual_models.append(Prophet())
 
-try:
-    from darts.models import BATS, TBATS, AutoARIMA
+models.append((AutoARIMA(), 12.2))
+models.append((TBATS(use_trend=True, use_arma_errors=True, use_box_cox=True), 8.0))
+models.append((BATS(use_trend=True, use_arma_errors=True, use_box_cox=True), 10.0))
+dual_models.append(AutoARIMA())
 
-    models.append((AutoARIMA(), 12.2))
-    models.append((TBATS(use_trend=True, use_arma_errors=True, use_box_cox=True), 8.0))
-    models.append((BATS(use_trend=True, use_arma_errors=True, use_box_cox=True), 10.0))
-    dual_models.append(AutoARIMA())
-    PMDARIMA_AVAILABLE = True
-except ImportError:
-    logger.warning("pmdarima not installed - will be skipping AutoARIMA tests")
-    PMDARIMA_AVAILABLE = False
 
 try:
     from darts.models import TCNModel  # noqa: F401
@@ -228,7 +221,6 @@ def test_dummy_series(self):
         with self.assertRaises(ValueError):
             varima.fit(series=ts)
 
-        if PMDARIMA_AVAILABLE:
-            autoarima = AutoARIMA(trend="t")
-            with self.assertRaises(ValueError):
-                autoarima.fit(series=ts)
+        autoarima = AutoARIMA(trend="t")
+        with self.assertRaises(ValueError):
+            autoarima.fit(series=ts)
diff --git a/darts/tests/models/forecasting/test_probabilistic_models.py b/darts/tests/models/forecasting/test_probabilistic_models.py
@@ -3,21 +3,13 @@
 from darts import TimeSeries
 from darts.logging import get_logger
 from darts.metrics import mae
-from darts.models import ARIMA, ExponentialSmoothing
+from darts.models import ARIMA, BATS, TBATS, ExponentialSmoothing
 from darts.models.forecasting.forecasting_model import GlobalForecastingModel
 from darts.tests.base_test_class import DartsBaseTestClass
 from darts.utils import timeseries_generation as tg
 
 logger = get_logger(__name__)
 
-try:
-    from darts.models import BATS, TBATS
-
-    PMDARIMA_AVAILABLE = True
-except ImportError:
-    logger.warning("pmdarima not available. BATS/TBATS probabilistic tests skipped.")
-    PMDARIMA_AVAILABLE = False
-
 try:
     import torch
 
@@ -58,31 +50,30 @@
     (ARIMA, {"p": 1, "d": 0, "q": 1}, 0.03),
 ]
 
-if PMDARIMA_AVAILABLE:
-    models_cls_kwargs_errs += [
-        (
-            BATS,
-            {
-                "use_trend": False,
-                "use_damped_trend": False,
-                "use_box_cox": True,
-                "use_arma_errors": False,
-                "random_state": 42,
-            },
-            0.3,
-        ),
-        (
-            TBATS,
-            {
-                "use_trend": False,
-                "use_damped_trend": False,
-                "use_box_cox": True,
-                "use_arma_errors": False,
-                "random_state": 42,
-            },
-            0.3,
-        ),
-    ]
+models_cls_kwargs_errs += [
+    (
+        BATS,
+        {
+            "use_trend": False,
+            "use_damped_trend": False,
+            "use_box_cox": True,
+            "use_arma_errors": False,
+            "random_state": 42,
+        },
+        0.3,
+    ),
+    (
+        TBATS,
+        {
+            "use_trend": False,
+            "use_damped_trend": False,
+            "use_box_cox": True,
+            "use_arma_errors": False,
+            "random_state": 42,
+        },
+        0.3,
+    ),
+]
 
 if TORCH_AVAILABLE:
     models_cls_kwargs_errs += [