Add a ML pipeline script to develop a whole pipeline.

exploration/ml_pipeline.py

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+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.13.0
+#   kernelspec:
+#     display_name: straw2analysis
+#     language: python
+#     name: straw2analysis
+# ---
+
+# %% 
+import sys, os
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+nb_dir = os.path.split(os.getcwd())[0]
+if nb_dir not in sys.path:
+    sys.path.append(nb_dir)
+
+from machine_learning.cross_validation import CrossValidation
+from machine_learning.preprocessing import Preprocessing
+
+# %% 
+df = pd.read_csv("../data/stressfulness_event_with_speech/input_appraisal_stressfulness_event_mean.csv")
+index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
+df.set_index(index_columns, inplace=True)
+
+cv = CrossValidation(data=df, cv_method="logo")
+
+categorical_columns = ["gender", "startlanguage", "mostcommonactivity", "homelabel"]
+interval_feature_list, other_feature_list = [], []
+
+print(df.columns.tolist())
+
+for split in cv.get_splits():
+    train_X, train_y, test_X, test_y = cv.get_train_test_sets(split)
+    pre = Preprocessing(train_X, train_y, test_X, test_y)
+    pre.one_hot_encode_train_and_test_sets(categorical_columns)
+    train_X, train_y, test_X, test_y = pre.get_train_test_sets()
+    break
+
+# %%