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