Revert "[WIP] CV model selection"

gtime/model_selection/__init__.py

@@ -4,9 +4,6 @@
 
 from .horizon_shift import horizon_shift
 from .splitters import FeatureSplitter
-from .cv_pipeline import CVPipeline
 from .cross_validation import time_series_split, blocking_time_series_split
 
-__all__ = ["FeatureSplitter", "horizon_shift",
-           "CVPipeline",
-           "time_series_split", "blocking_time_series_split"]
+__all__ = ["FeatureSplitter", "horizon_shift"]

gtime/model_selection/cross_validation.py

@@ -1,6 +1,4 @@
 import pandas as pd
-from sklearn.model_selection import train_test_split
-from typing import Union
 
 
 def _time_series_split_on_index(time_series, n_samples, n_splits):

gtime/time_series_models/base.py

@@ -1,14 +1,13 @@
 from typing import List, Tuple, Union
 
 import pandas as pd
-import numpy as np
 from sklearn.base import BaseEstimator, RegressorMixin
 from sklearn.linear_model import LinearRegression
 from sklearn.utils.validation import check_is_fitted
 
 from gtime.compose import FeatureCreation
 from gtime.model_selection import horizon_shift, FeatureSplitter
-from gtime.metrics import rmse
+
 
 class TimeSeriesForecastingModel(BaseEstimator, RegressorMixin):
     """ Base class for a generic time series forecasting model.
@@ -88,12 +87,12 @@ def fit(self, X: pd.DataFrame, y: pd.DataFrame = None, only_model: bool = False)
         self
         """
         if not only_model:
-            X_train, y_train, X_test, y_test = self._compute_train_test_matrices(X, y)
+            X_train, y_train, X_test = self._compute_train_test_matrices(X, y)
         elif not self.cache_features:
             raise AttributeError("cache_feature must be True to fit only model")
         else:
             check_is_fitted(self)  # only_model works if the model is already fitted
-            X_train, y_train, X_test, y_test = self.X_train_, self.y_train_, self.X_test_, self.y_test_
+            X_train, y_train, X_test = self.X_train_, self.y_train_, self.X_test_
 
         self.model_ = self._fit_model(X_train, y_train)
         self.X_test_ = X_test
@@ -127,15 +126,14 @@ def set_params(self, **params):
 
     def _compute_train_test_matrices(
         self, X: pd.DataFrame, y: pd.DataFrame
-    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
         X, y = self._create_X_y_feature_matrices(X, y)
         X_train, y_train, X_test, y_test = self._split_train_test(X, y)
         if self.cache_features:
             self.X_train_ = X_train
             self.y_train_ = y_train
             self.X_test_ = X_test
-            self.y_test_ = y_test
-        return X_train, y_train, X_test, y_test
+        return X_train, y_train, X_test
 
     def _create_X_y_feature_matrices(
         self, X, y=None
@@ -159,37 +157,3 @@ def _reset(self):
         ]
         for attribute in attributes:
             delattr(self, attribute)
-
-    def score(self, X=None, y=None, metrics=None):
-
-        check_is_fitted(self)
-        if X is None:
-            y_pred_test = self.predict()
-        else:
-            y_pred_test = self.predict(X)
-        y_pred_train = self.model_.predict(self.X_train_)
-
-        y_test = self.y_test_ if y is None else y
-
-        if metrics is None:
-            metrics = {'rmse': rmse}
-        score = pd.DataFrame(columns=metrics.keys(), index=['Train score', 'Test score'])
-        score.loc[['Train score'], :] = self._score(self.y_train_, y_pred_train, metrics=metrics, type='Train score')
-        score.loc[['Test score'], :] = self._score(y_test, y_pred_test, metrics=metrics, type='Test score')
-        return score.T
-
-    def _score(self, y, y_pred, metrics=None, type='Test'):
-        score = pd.DataFrame(columns=metrics.keys(), index=[type])
-        for name, metric in metrics.items():
-            scores = []
-            for col in y.columns:
-                na_mask = ~y[col].isna()
-                y_pred_i = y_pred[col][na_mask]
-                y_true_i = y[col][na_mask]
-                if len(y_true_i) > 0:
-                    scores.append(metric(y_true_i, y_pred_i))
-            score[name] = np.mean(scores)
-        return score
-
-    def set_model(self, model):
-        self.model = model