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ml_farhan.py
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ml_farhan.py
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"""
Implementation of prior depression detection algorithm:
Asma Ahmad Farhan, Chaoqun Yue, Reynaldo Morillo, Shweta Ware, Jin Lu, Jinbo Bi,
Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2016.
Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data.
In 2016 IEEE Wireless Health (WH). IEEE, 1–8. https://doi.org/10.1109/WH.2016.7764553
"""
import os, sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from utils.common_settings import *
from algorithm.ml_basic import DepressionDetectionAlgorithm_ML_basic
from data_loader.data_loader_ml import DataRepo
from algorithm.base import DepressionDetectionClassifierBase
from warnings import simplefilter
from sklearn.exceptions import ConvergenceWarning
simplefilter("ignore", category=ConvergenceWarning)
class DepressionDetectionClassifier_ML_farhan(DepressionDetectionClassifierBase):
"""Classifier for Farhan et al. work. Two SVM, one for ios, and the other for android """
def __init__(self, model_params, selected_features):
self.model_params = model_params
self.selected_features = selected_features
self.svm_ios = utils_ml.get_clf("svm", model_params, direct_param_flag = True)
self.svm_android = utils_ml.get_clf("svm", model_params, direct_param_flag = True)
self.device_type_set = set([0,1]) # ios - 1, android - 0
self.single_class_flag_android = False
self.single_class_android = None
self.single_class_flag_ios = False
self.single_class_ios = None
self.no_data_flag_ios = False
self.no_data_flag_android = False
def fit(self, X, y=None):
assert set(self.selected_features).issubset(set(X.columns))
assert X.columns[-1] == "device_type"
set_random_seed(42)
X_np = np.array(X[self.selected_features])
y_np = np.array(y)
devices = X_np[:,-1]
assert set(devices).issubset(self.device_type_set)
devices_ios_index = np.where(devices == 1)[0]
devices_android_index = np.where(devices != 1)[0]
X_ios = X_np[devices_ios_index,:-1]
y_ios = y_np[devices_ios_index]
X_android = X_np[devices_android_index,:-1]
y_android = y_np[devices_android_index]
if (len(devices_ios_index) == 0):
self.no_data_flag_ios = True
else:
self.no_data_flag_ios = False
if (len(devices_android_index) == 0):
self.no_data_flag_android = True
else:
self.no_data_flag_android = False
if (not self.no_data_flag_ios):
if (len(np.unique(y_ios)) == 1):
self.single_class_flag_ios = True
self.single_class_ios = y_ios[0]
else:
self.svm_ios.fit(X_ios, y_ios)
if (self.no_data_flag_android): # only ios data
self.svm_android = self.svm_ios
if (not self.no_data_flag_android):
if (len(np.unique(y_android)) == 1):
self.single_class_flag_android = True
self.single_class_android = y_android[0]
else:
self.svm_android.fit(X_android, y_android)
if (self.no_data_flag_ios): # only android data
self.svm_android = self.svm_android
def predict(self, X, y=None):
X_np = np.array(X)
y_pred = np.empty(len(X_np))
devices = X_np[:,-1]
devices_ios_index = np.where(devices == 1)[0]
devices_android_index = np.where(devices != 1)[0]
if (len(devices_ios_index) > 0):
X_ios = X_np[devices_ios_index,:-1]
if (self.single_class_flag_ios):
y_pred[devices_ios_index] = np.array([self.single_class_ios for _ in range(len(X_ios))])
else:
y_pred[devices_ios_index] = self.svm_ios.predict(X_ios)
if (len(devices_android_index) > 0):
X_android = X_np[devices_android_index,:-1]
if (self.single_class_flag_android):
y_pred[devices_android_index] = np.array([self.single_class_android for _ in range(len(X_android))])
else:
y_pred[devices_android_index] = self.svm_android.predict(X_android)
return y_pred
def predict_proba(self, X, y=None):
X_np = np.array(X)
y_pred_prob = np.empty((len(X_np),2))
devices = X_np[:,-1]
devices_ios_index = np.where(devices == 1)[0]
devices_android_index = np.where(devices != 1)[0]
if (len(devices_ios_index) > 0):
X_ios = X_np[devices_ios_index,:-1]
if (self.single_class_flag_ios):
y_pred_prob[devices_ios_index] = np.array([[0,1] if self.single_class_ios else [1,0] for _ in range(len(X_ios))])
else:
y_pred_prob[devices_ios_index] = self.svm_ios.predict_proba(X_ios)
if (len(devices_android_index) > 0):
X_android = X_np[devices_android_index,:-1]
if (self.single_class_flag_android):
y_pred_prob[devices_android_index] = np.array([[0,1] if self.single_class_android else [1,0] for _ in range(len(X_android))])
else:
y_pred_prob[devices_android_index] = self.svm_android.predict_proba(X_android)
return y_pred_prob
class DepressionDetectionAlgorithm_ML_farhan(DepressionDetectionAlgorithm_ML_basic):
"""Algirithm for Farhan et al. work, extending the basic traditional ml algorithm """
def __init__(self, config_dict = None, config_name = "ml_farhan"):
super().__init__(config_dict, config_name)
def prep_model(self, data_train: DataRepo, criteria: str = "f1") -> sklearn.base.ClassifierMixin:
super().prep_model()
set_random_seed(42)
@ray.remote
def train_small_cv(data_repo: DataRepo, model_parameters: dict):
warnings.filterwarnings("ignore")
X = data_repo.X
y = data_repo.y
pids = data_repo.pids
clf = DepressionDetectionClassifier_ML_farhan(model_params=model_parameters, selected_features=X.columns)
cv = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=42)
r = cross_validate(clf, X=X, y=y, groups=pids, cv = cv,
scoring = utils_ml.results_report_sklearn_noprob, return_train_score=False)
r = {k:np.mean(v) for k,v in r.items()}
r.update({"parameters":model_parameters})
return r
C_list = [2**i for i in range(-15,16)]
# gamma_list = [2**i for i in range(-15,16)]
gamma_list = ["scale"]
parameters_list = []
for C, gamma in itertools.product(C_list, gamma_list):
parameters_tmp = {"kernel":"rbf", "max_iter":100000,"C":C, "gamma": gamma, "cache_size":700, "random_state":42}
parameters_list.append(parameters_tmp)
data_train_id = ray.put(data_train)
results_list = ray.get([train_small_cv.remote(data_train_id,i) for i in parameters_list])
results_list = pd.DataFrame(results_list)
best_row = results_list.iloc[results_list[f"test_{criteria}"].argmax()]
best_params = best_row['parameters']
if (self.verbose > 0):
print(best_row)
print(best_params)
best_params["probability"] = True
return DepressionDetectionClassifier_ML_farhan(model_params=best_params, selected_features=data_train.X.columns)