[WIP] Finish the class by assigning columns and validating model.

machine_learning/pipeline.py

@@ -1,6 +1,7 @@
 import datetime
 
 import pandas as pd
+from sklearn.model_selection import cross_val_score
 
 import participants.query_db
 from features import esm, helper, proximity
@@ -8,7 +9,15 @@ from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
 
 
 class MachineLearningPipeline:
-    def __init__(self, labels_questionnaire, data_types, participants_usernames=None):
+    def __init__(
+        self,
+        labels_questionnaire,
+        labels_scale,
+        data_types,
+        participants_usernames=None,
+        feature_names=None,
+        grouping_variable=None,
+    ):
         if participants_usernames is None:
             participants_usernames = participants.query_db.get_usernames(
                 collection_start=datetime.date.fromisoformat("2020-08-01")
@@ -17,6 +26,17 @@ class MachineLearningPipeline:
         self.labels_questionnaire = labels_questionnaire
         self.data_types = data_types
 
+        if feature_names is None:
+            self.feature_names = []
+        self.df_features = pd.DataFrame()
+        self.labels_scale = labels_scale
+        self.df_labels = pd.DataFrame()
+        self.grouping_variable = grouping_variable
+        self.df_groups = pd.DataFrame()
+
+        self.model = None
+        self.validation_method = None
+
         self.df_esm = pd.DataFrame()
         self.df_esm_preprocessed = pd.DataFrame()
         self.df_esm_interest = pd.DataFrame()
@@ -77,3 +97,29 @@ class MachineLearningPipeline:
             self.df_full_data_daily_means = self.df_full_data_daily_means.join(
                 self.df_proximity_daily_counts
             )
+
+    def assign_columns(self):
+        self.df_features = self.df_full_data_daily_means[self.feature_names]
+        self.df_labels = self.df_full_data_daily_means[self.labels_scale]
+        if self.grouping_variable:
+            self.df_groups = self.df_full_data_daily_means[self.grouping_variable]
+        else:
+            self.df_groups = None
+
+    def validate_model(self):
+        if self.model is None:
+            raise AttributeError(
+                "Please, specify a machine learning model first, by setting the .model attribute."
+            )
+        if self.validation_method is None:
+            raise AttributeError(
+                "Please, specify a cross validation method first, by setting the .validation_method attribute."
+            )
+        cross_val_score(
+            estimator=self.model,
+            X=self.df_features,
+            y=self.df_labels,
+            groups=self.df_groups,
+            cv=self.validation_method,
+            n_jobs=-1,
+        )