Updated requirements

Signed-off-by: Stefano Savare <[email protected]>

examples/example.py

@@ -0,0 +1,61 @@
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+
+from gtime.feature_extraction import Shift
+from gtime.time_series_models.base import TimeSeriesForecastingModel
+
+
+class TimeSeries(pd.DataFrame):
+    def plot(self):
+        pass
+
+
+time_series = TimeSeries()
+
+# You can plot
+time_series.plot()
+
+# Decomposition
+## Un peu bizarre le plot_stl() et deux fois stl_decomposition
+time_series = time_series.stl_decomposition()
+time_series.plot_stl()
+time_series = time_series.recompose()  # Choose a good name
+
+# Box-Cox
+time_series = time_series.box_cox(lambda_=0.3)
+
+# Feature forecasting
+features = [("shift", Shift(1), "time_series")]
+automatic_features = get_features()  # Similar to fast.ai get_transforms()
+gar_forecaster = LinearRegression()
+# This object TimeSeriesForecastingModel keeps into account all the intermediate steps.
+# You don't need to manually deal with train/test split, etc..
+forecasting_model = TimeSeriesForecastingModel(
+    features=features, horizon=3, model=gar_forecaster
+)
+forecasting_model = forecasting_model.fit(time_series)
+forecasting_model.predict()
+forecasting_model.cross_validate()  # Is cross validation also on multiple time series?
+
+# Residuals analysis
+forecasting_model.residuals_.acf()
+
+# Questions
+"""
+How to implement ARIMA? I think that a GAR forecaster with MA should work, but we should check. 
+It helps that the user can't customize the feature matrix.
+
+Exponential Smoothing? Maybe it could work also? Not clear if it is possible with additional
+features
+
+Add a learner object?
+"""
+
+time_series = TimeSeries(pandas_dataframe)
+
+arima = ARIMA(time_series)
+arima.fit(time_series,,
+preds = arima.predict(time_series)
+
+
+time_series.to_pandas()

gtime/explainability/tests/test_explainer.py

@@ -1,7 +1,8 @@
 import hypothesis.strategies as st
 import numpy as np
+import pandas as pd
 import pytest
-from hypothesis import given
+from hypothesis import given, settings
 from hypothesis.strategies import floats, sampled_from, data, lists, text
 from lime.explanation import Explanation
 from shap.explainers.explainer import Explainer
@@ -130,6 +131,7 @@ def _check_predict_output(
             ]
         )
 
+    @settings(deadline=pd.Timedelta(milliseconds=5000), max_examples=7)
     @pytest.mark.parametrize(
         "explainer", lazy_fixtures([lime_explainer, shap_explainer])
     )
@@ -153,6 +155,7 @@ def test_error_predict_not_fit(self, explainer, X):
 
 
 class TestLime:
+    @settings(deadline=pd.Timedelta(milliseconds=10000), max_examples=7)
     @given(regressor=models(), X_y=numpy_X_y_matrices(min_value=-100, max_value=100))
     def test_predict(self, lime_explainer, regressor, X_y):
         X, y = X_y

gtime/feature_extraction/tests/test_trend.py

@@ -37,7 +37,7 @@ def test_polynomial_detrend():
         columns=[f"0__{feature_name}"],
         index=time_index,
     )
-    pd.testing.assert_frame_equal(ts_t, expected_ts)
+    pd.testing.assert_frame_equal(ts_t, expected_ts, check_less_precise=3)
 
 
 def test_exponential_detrend():

gtime/hierarchical/tests/test_top_down.py

@@ -255,7 +255,7 @@ def test_error_predict_not_fitted(self, hierarchical_basic_top_down_model):
         with pytest.raises(sklearn.exceptions.NotFittedError):
             hierarchical_basic_top_down_model.predict()
 
-    @given(dataframes=n_time_series_with_same_index())
+    @given(dataframes=n_time_series_with_same_index(min_n=5))
     def test_error_with_bad_predict_key(
         self, dataframes, hierarchical_basic_top_down_model
     ):

gtime/regressors/tests/test_explainable.py

@@ -1,7 +1,7 @@
 from typing import List
 
 import pytest
-from hypothesis import given
+from hypothesis import given, settings
 from sklearn import clone
 from sklearn.base import BaseEstimator
 from sklearn.cluster import DBSCAN, KMeans, SpectralClustering
@@ -85,6 +85,7 @@ def test_fit_values(self, estimator, explainer_type, X_y):
             estimator_fit_attributes, cloned_estimator_fit_attributes
         )
 
+    @settings(deadline=pd.Timedelta(milliseconds=5000), max_examples=7)
     @pytest.mark.parametrize("explainer_type", ["lime", "shap"])
     @given(
         estimator=regressors(), X_y=numpy_X_y_matrices(min_value=-100, max_value=100)

requirements.txt

@@ -4,3 +4,5 @@ scikit-learn>=0.22.0
 matplotlib>=3.1.0
 lime>=0.2.0.0
 shap>=0.35
+holidays>=0.10.2
+lunarcalendar>=0.0.9