128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
# ---
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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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# %% jupyter={"source_hidden": true}
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# %matplotlib inline
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import datetime
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import importlib
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import os
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import sys
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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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import seaborn as sns
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from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
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from sklearn.model_selection import LeaveOneGroupOut, cross_validate
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from sklearn.dummy import DummyClassifier
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from sklearn.impute import SimpleImputer
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import xgboost as xg
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from IPython.core.interactiveshell import InteractiveShell
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InteractiveShell.ast_node_interactivity = "all"
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from pathlib import Path
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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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import machine_learning.labels
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import machine_learning.model
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from machine_learning.helper import run_all_classification_models
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# %% [markdown]
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# # RAPIDS models
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# %% [markdown]
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# ## Set script's parameters
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#
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# %%
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cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
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n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
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# %% jupyter={"source_hidden": true}
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filename = Path("E:/STRAWresults/inputData/stressfulness_event/input_appraisal_stressfulness_event_mean.csv")
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model_input = pd.read_csv(filename)
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# %% jupyter={"source_hidden": true}
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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model_input.set_index(index_columns, inplace=True)
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model_input['target'].value_counts()
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# %% jupyter={"source_hidden": true}
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bins = [-10, -1, 1, 10] # bins for z-scored targets
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# bins = [0, 1, 4] # bins for stressfulness (1-4) target
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model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
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model_input['target'].value_counts(), edges
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model_input = model_input[model_input['target'] != "medium"]
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model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
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model_input['target'].value_counts()
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if cv_method_str == 'halflogo':
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model_input['pid_index'] = model_input.groupby('pid').cumcount()
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model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
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model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
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model_input["pid_half"] = model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
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data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
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else:
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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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# %% jupyter={"source_hidden": true}
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categorical_feature_colnames = ["gender", "startlanguage"]
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additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
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categorical_feature_colnames += additional_categorical_features
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categorical_features = data_x[categorical_feature_colnames].copy()
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mode_categorical_features = categorical_features.mode().iloc[0]
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# fillna with mode
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categorical_features = categorical_features.fillna(mode_categorical_features)
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# one-hot encoding
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categorical_features = categorical_features.apply(lambda col: col.astype("category"))
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
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train_x = pd.concat([numerical_features, categorical_features], axis=1)
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# %% jupyter={"source_hidden": true}
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cv_method = None # Defaults to 5 k-folds in cross_validate method
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if cv_method_str == 'logo' or cv_method_str == 'half_logo':
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cv_method = LeaveOneGroupOut()
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cv_method.get_n_splits(
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train_x,
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data_y,
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groups=data_groups,
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)
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='median')
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# %%
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final_scores = run_all_classification_models(imputer.fit_transform(train_x), data_y, data_groups, cv_method)
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# %%
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final_scores.index.name = "metric"
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final_scores = final_scores.set_index(["method", final_scores.index])
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final_scores.to_csv("event_stressfulness_lmh_lh_scores.csv")
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