NEW: reset global seed before every test case

CHANGELOG.md

@@ -33,6 +33,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Colorebar scaling in Correlation heatmap plotter ([#143](https://github.com/tinkoff-ai/etna-ts/pull/143))
 - Add Correlation heatmap in EDA notebook ([#144](https://github.com/tinkoff-ai/etna-ts/pull/144))
 - Add `__repr__` for Pipeline ([#151](https://github.com/tinkoff-ai/etna-ts/pull/151))
+- Defined random state for every test cases ([#155](https://github.com/tinkoff-ai/etna-ts/pull/155))
 
 ### Fixed
 - Add more obvious Exception Error for forecasting with unfitted model ([#102](https://github.com/tinkoff-ai/etna-ts/pull/102))

etna/datasets/tsdataset.py

@@ -69,7 +69,6 @@ class TSDataset:
     """
 
     idx = pd.IndexSlice
-    np.random.seed(0)
 
     def __init__(self, df: pd.DataFrame, freq: str, df_exog: Optional[pd.DataFrame] = None):
         """Init TSDataset.
@@ -313,7 +312,7 @@ def regressors(self) -> List[str]:
         """
         return self._regressors
 
-    def plot(self, n_segments: int = 10, column: str = "target", segments: Optional[Sequence] = None):
+    def plot(self, n_segments: int = 10, column: str = "target", segments: Optional[Sequence] = None, seed: int = 1):
         """Plot of random or chosen segments.
 
         Parameters
@@ -324,6 +323,8 @@ def plot(self, n_segments: int = 10, column: str = "target", segments: Optional[
             feature to plot
         segments:
             segments to plot
+        seed:
+            seed for local random state
         """
         if not segments:
             segments = self.segments
@@ -332,7 +333,8 @@ def plot(self, n_segments: int = 10, column: str = "target", segments: Optional[
         rows_num = math.ceil(k / columns_num)
         _, ax = plt.subplots(rows_num, columns_num, figsize=(20, 5 * rows_num), squeeze=False)
         ax = ax.ravel()
-        for i, segment in enumerate(sorted(np.random.choice(segments, size=k, replace=False))):
+        rnd_state = np.random.RandomState(seed)
+        for i, segment in enumerate(sorted(rnd_state.choice(segments, size=k, replace=False))):
             df_slice = self[:, segment, column]
             ax[i].plot(df_slice.index, df_slice.values)
             ax[i].set_title(segment)

tests/conftest.py

@@ -5,8 +5,21 @@
 from etna.datasets.tsdataset import TSDataset
 
 
+@pytest.fixture(autouse=True)
+def random_seed():
+    "Fixture to fix random state for every test case"
+    import random
+
+    import torch
+
+    SEED = 121  # noqa: N806
+    torch.manual_seed(SEED)
+    random.seed(SEED)
+    np.random.seed(SEED)
+
+
 @pytest.fixture()
-def example_df():
+def example_df(random_seed):
     df1 = pd.DataFrame()
     df1["timestamp"] = pd.date_range(start="2020-01-01", end="2020-02-01", freq="H")
     df1["segment"] = "segment_1"
@@ -21,7 +34,7 @@ def example_df():
 
 
 @pytest.fixture
-def two_dfs_with_different_timestamps():
+def two_dfs_with_different_timestamps(random_seed):
     """Generate two dataframes with the same segments and different timestamps"""
 
     def generate_df(start_time):
@@ -44,7 +57,7 @@ def generate_df(start_time):
 
 
 @pytest.fixture
-def two_dfs_with_different_segments_sets():
+def two_dfs_with_different_segments_sets(random_seed):
     """Generate two dataframes with the same timestamps and different segments"""
 
     def generate_df(n_segments):
@@ -67,7 +80,7 @@ def generate_df(n_segments):
 
 
 @pytest.fixture
-def train_test_dfs():
+def train_test_dfs(random_seed):
     """Generate two dataframes with the same segments and the same timestamps"""
 
     def generate_df():
@@ -131,7 +144,7 @@ def outliers_df():
 
 
 @pytest.fixture
-def example_df_() -> pd.DataFrame:
+def example_df_(random_seed) -> pd.DataFrame:
     periods = 100
     df1 = pd.DataFrame({"timestamp": pd.date_range("2020-01-01", periods=periods)})
     df1["segment"] = ["segment_1"] * periods
@@ -150,7 +163,7 @@ def example_df_() -> pd.DataFrame:
 
 
 @pytest.fixture
-def example_tsds() -> TSDataset:
+def example_tsds(random_seed) -> TSDataset:
     periods = 100
     df1 = pd.DataFrame({"timestamp": pd.date_range("2020-01-01", periods=periods)})
     df1["segment"] = "segment_1"
@@ -168,7 +181,7 @@ def example_tsds() -> TSDataset:
 
 
 @pytest.fixture
-def example_reg_tsds() -> TSDataset:
+def example_reg_tsds(random_seed) -> TSDataset:
     periods = 100
     df1 = pd.DataFrame({"timestamp": pd.date_range("2020-01-01", periods=periods)})
     df1["segment"] = "segment_1"

tests/test_analysis/test_outliers/test_hist_outliers.py

@@ -50,11 +50,10 @@ def test_v_optimal_hist(series: np.array, bins_number: int, expected: np.array):
     np.testing.assert_almost_equal(error, expected)
 
 
-@pytest.mark.parametrize(
-    "series,k", ((np.random.random(100), 10), (np.random.random(100), 20), (np.random.random(10), 4))
-)
-def test_compute_f_format(series: np.array, k: int):
+@pytest.mark.parametrize("series_len,k", ((100, 10), (100, 20), (10, 4)))
+def test_compute_f_format(random_seed, series_len: int, k: int):
     """Check that computeF produce the correct size output."""
+    series = np.random.random(size=series_len)
     p, pp = np.empty_like(series), np.empty_like(series)
     p[0] = series[0]
     pp[0] = series[0] ** 2

tests/test_clustering/test_clustering.py

@@ -9,7 +9,7 @@
 
 
 @pytest.fixture
-def eucl_ts() -> TSDataset:
+def eucl_ts(random_seed) -> TSDataset:
     df = pd.DataFrame()
     for i in range(1, 8):
         date_range = pd.date_range("2020-01-01", "2020-05-01")

tests/test_datasets/test_dataset.py

@@ -10,7 +10,7 @@
 
 
 @pytest.fixture()
-def tsdf_with_exog() -> TSDataset:
+def tsdf_with_exog(random_seed) -> TSDataset:
     df_1 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="1d")})
     df_2 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="1d")})
     df_1["segment"] = "Moscow"

tests/test_model_selection/test_backtest.py

@@ -25,7 +25,7 @@
 
 
 @pytest.fixture
-def imbalanced_tsdf() -> TSDataset:
+def imbalanced_tsdf(random_seed) -> TSDataset:
     """Generate two series with big time range difference"""
     df1 = pd.DataFrame({"timestamp": pd.date_range("2021-01-25", "2021-02-01", freq="D")})
     df1["segment"] = "segment_1"
@@ -43,7 +43,7 @@ def imbalanced_tsdf() -> TSDataset:
 
 
 @pytest.fixture()
-def big_daily_example_tsdf() -> TSDataset:
+def big_daily_example_tsdf(random_seed) -> TSDataset:
     df1 = pd.DataFrame()
     df1["timestamp"] = pd.date_range(start="2019-01-01", end="2020-04-01", freq="D")
     df1["segment"] = "segment_1"
@@ -62,7 +62,7 @@ def big_daily_example_tsdf() -> TSDataset:
 
 
 @pytest.fixture()
-def example_tsdf() -> TSDataset:
+def example_tsdf(random_seed) -> TSDataset:
     df1 = pd.DataFrame()
     df1["timestamp"] = pd.date_range(start="2020-01-01", end="2020-02-01", freq="H")
     df1["segment"] = "segment_1"
@@ -81,7 +81,7 @@ def example_tsdf() -> TSDataset:
 
 
 @pytest.fixture()
-def big_example_tsdf() -> TSDataset:
+def big_example_tsdf(random_seed) -> TSDataset:
     df1 = pd.DataFrame()
     df1["timestamp"] = pd.date_range(start="2020-01-01", end="2021-02-01", freq="D")
     df1["segment"] = "segment_1"

tests/test_models/nn/test_deepar.py

@@ -1,8 +1,4 @@
-import random
-
-import numpy as np
 import pytest
-import torch
 from pytorch_forecasting.data import GroupNormalizer
 
 from etna.datasets.tsdataset import TSDataset
@@ -39,10 +35,6 @@ def test_deepar_model_run_weekly_overfit(weekly_period_df, horizon):
     When:
     Then: I get {horizon} periods per dataset as a forecast and they "the same" as past
     """
-    SEED = 121  # noqa: N806
-    torch.manual_seed(SEED)
-    random.seed(SEED)
-    np.random.seed(SEED)
 
     ts_start = sorted(set(weekly_period_df.timestamp))[-horizon]
     train, test = (

tests/test_models/nn/test_tft.py

@@ -1,8 +1,4 @@
-import random
-
-import numpy as np
 import pytest
-import torch
 
 from etna.datasets.tsdataset import TSDataset
 from etna.metrics import MAE
@@ -41,11 +37,6 @@ def test_tft_model_run_weekly_overfit(weekly_period_df, horizon):
     Then: I get {horizon} periods per dataset as a forecast and they "the same" as past
     """
 
-    SEED = 121  # noqa: N806
-    torch.manual_seed(SEED)
-    random.seed(SEED)
-    np.random.seed(SEED)
-
     ts_start = sorted(set(weekly_period_df.timestamp))[-horizon]
     train, test = (
         weekly_period_df[lambda x: x.timestamp < ts_start],

tests/test_models/test_linear_model.py

@@ -35,18 +35,16 @@ def linear_segments_by_parameters(alpha_values, intercept_values):
 
 
 @pytest.fixture()
-def linear_segments_ts_unique():
+def linear_segments_ts_unique(random_seed):
     """Create TSDataset that represents 3 segments with unique linear dependency on lags in each."""
-    np.random.seed(42)
     alpha_values = [np.random.rand() * 4 - 2 for _ in range(3)]
     intercept_values = [np.random.rand() * 4 + 1 for _ in range(3)]
     return linear_segments_by_parameters(alpha_values, intercept_values)
 
 
 @pytest.fixture()
-def linear_segments_ts_common():
+def linear_segments_ts_common(random_seed):
     """Create TSDataset that represents 3 segments with common linear dependency on lags in each."""
-    np.random.seed(42)
     alpha_values = [np.random.rand() * 4 - 2] * 3
     intercept_values = [np.random.rand() * 4 + 1 for _ in range(3)]
     return linear_segments_by_parameters(alpha_values, intercept_values)

tests/test_models/test_sarimax_model.py

@@ -1,9 +1,4 @@
-import numpy as np
-
-from etna.datasets import TSDataset
-from etna.metrics import MAE
 from etna.models import SARIMAXModel
-from etna.transforms import TheilSenTrendTransform
 
 
 def test_sarimax_forecaster_run(example_tsds):
@@ -39,30 +34,3 @@ def test_sarimax_forecaster_run_with_reg(example_reg_tsds):
 
     assert not res.isnull().values.any()
     assert len(res) == 14
-
-
-def test_compare_sarimax_vanilla_reg(example_reg_tsds):
-    horizon = 24
-    example_tsds = TSDataset(example_reg_tsds[:, :, "target"], freq="D")
-    train, test = example_tsds.train_test_split(
-        train_start=None, train_end="2020-01-31", test_start="2020-02-01", test_end="2020-02-24"
-    )
-    model = SARIMAXModel()
-    model.fit(train)
-    future_ts = train.make_future(future_steps=horizon)
-    vanilla_result = model.forecast(future_ts)
-
-    train, test = example_reg_tsds.train_test_split(
-        train_start=None, train_end="2020-01-31", test_start="2020-02-01", test_end="2020-02-24"
-    )
-    prep = TheilSenTrendTransform(in_column="target")
-    train.fit_transform([prep])
-    model = SARIMAXModel()
-    model.fit(train)
-    future_ts = train.make_future(future_steps=horizon)
-    reg_result = model.forecast(future_ts)
-
-    van_acc = np.array(list(MAE()(test, vanilla_result).values()))
-    reg_acc = np.array(list(MAE()(test, reg_result).values()))
-
-    assert np.all(van_acc < reg_acc)

tests/test_transforms/test_impute_transform.py

@@ -56,7 +56,7 @@ def test_wrong_init_two_segments(all_date_present_df_two_segments):
 
 
 @pytest.fixture()
-def df_with_missing_value_x_index(all_date_present_df: pd.DataFrame) -> Tuple[pd.DataFrame, int]:
+def df_with_missing_value_x_index(random_seed, all_date_present_df: pd.DataFrame) -> Tuple[pd.DataFrame, int]:
     """Create pd.DataFrame that contains some target on given range of dates with one gap."""
     # index cannot be first or last value,
     # because Imputer should know starting and ending dates

tests/test_transforms/test_log_transform.py

@@ -8,7 +8,7 @@
 
 
 @pytest.fixture
-def non_positive_df_() -> pd.DataFrame:
+def non_positive_df_(random_seed) -> pd.DataFrame:
     """Generate dataset with non-positive target."""
     periods = 100
     df1 = pd.DataFrame({"timestamp": pd.date_range("2020-01-01", periods=periods)})
@@ -26,7 +26,7 @@ def non_positive_df_() -> pd.DataFrame:
 
 
 @pytest.fixture
-def positive_df_() -> pd.DataFrame:
+def positive_df_(random_seed) -> pd.DataFrame:
     """Generate dataset with positive target."""
     periods = 100
     df1 = pd.DataFrame({"timestamp": pd.date_range("2020-01-01", periods=periods)})