-
Notifications
You must be signed in to change notification settings - Fork 0
/
c4_full_runs_to_gluc2.py
163 lines (134 loc) · 8.26 KB
/
c4_full_runs_to_gluc2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.layers import Dense, Input, Conv1D, Flatten, concatenate
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model
df_meals_train = pd.read_csv("data/c3_meals_train.csv")
df_meals_train["start_time"] = pd.to_datetime(df_meals_train["start_time"])
df_glucose_train = pd.read_csv("data/c3_glucose_train.csv")
df_glucose_train["time"] = pd.to_datetime(df_glucose_train["time"])
df_final_test_meal = pd.read_csv("data/c3_final_meal_test.csv")
df_final_test_meal["start_time"] = pd.to_datetime(df_final_test_meal["start_time"])
df_final_test_glucose = pd.read_csv("data/c3_final_glucose_test.csv")
df_final_test_glucose["time"] = pd.to_datetime(df_final_test_glucose["time"])
df_glucose_test = pd.read_csv("data/c3_glucose_test.csv")
df_glucose_test["time"] = pd.to_datetime(df_glucose_test["time"])
df_meals_test = pd.read_csv("data/c3_meals_test.csv")
df_meals_test["start_time"] = pd.to_datetime(df_meals_test["start_time"])
time_after_meal = pd.Timedelta("180 minutes")
time_points_needed_train = int(time_after_meal.seconds/60) + 1
time_points_needed_test = int(time_after_meal.seconds/60/60)*4
df_glucose_train_list_out = []
df_features_train = []
for user_id in df_meals_train["user_id"].unique():
df_user_meals_train = df_meals_train[df_meals_train["user_id"] == user_id]
df_user_glucose_train = df_glucose_train[df_glucose_train["user_id"] == user_id]
start_time = df_user_glucose_train["time"].min()
end_time = df_user_glucose_train["time"].max()
time_cutoff = start_time + (end_time - start_time) * 2/3
# take glucose until 2/3 of the way through for features
df_gluc_features = df_user_glucose_train[df_user_glucose_train["time"] < time_cutoff]
df_gluc_features = df_gluc_features["bg"].quantile([0.1, 0.25, 0.5, 0.75, 0.9])
df_gluc_features = df_gluc_features.rename({0.1: "bg_10th", 0.25: "bg_25th", 0.5: "bg_50th", 0.75: "bg_75th", 0.9: "bg_90th"})
# use macronutrient averages as features up until 2/3 of the way through
df_meals_features = df_user_meals_train[df_user_meals_train["start_time"] < time_cutoff]
df_meals_features = df_meals_features[["carbohydrate_g", "fat_g", "protein_g", "fiber_g"]].mean()
df_meals_features = df_meals_features.rename({"carbohydrate_g": "carbohydrate_g_mean", "fat_g": "fat_g_mean",
"protein_g": "protein_g_mean", "fiber_g": "fiber_g_mean"})
if df_meals_features.isna().any():
continue
# for each meal in the last third, check if enough glucose data exists before and after to add it as data point
df_glucose_last_third = df_user_glucose_train[df_user_glucose_train["time"] >= time_cutoff]
df_meals_last_third = df_user_meals_train[df_user_meals_train["start_time"] >= time_cutoff]
for index, row in df_meals_last_third.iterrows():
# check if there are enough glucose data points before and after meal
meal_time = row["start_time"]
glucose_end = meal_time + time_after_meal
df_glucose_meal_after = df_glucose_last_third[(df_glucose_last_third["time"] >= meal_time) & (df_glucose_last_third["time"] <= glucose_end)]
if len(df_glucose_meal_after) >= time_points_needed_train:
# resample before and after meal in 15 minute intervals
df_glucose_meal_after = df_glucose_meal_after.set_index("time")
shift = pd.Timedelta(minutes=meal_time.minute % 15, seconds=meal_time.second)
df_glucose_meal_after.index = df_glucose_meal_after.index - shift
df_glucose_meal_after = df_glucose_meal_after.resample("15T").mean()
df_glucose_meal_after.index = df_glucose_meal_after.index + shift
df_glucose_meal_after = df_glucose_meal_after.reset_index()
df_glucose_train_list_out.append(df_glucose_meal_after.iloc[1:]["bg"].rename(user_id).reset_index(drop=True))
# transform datetime of meal_time to minute count of the day
meal_time = meal_time.hour * 60 + meal_time.minute
time_encoding = np.array([np.sin(meal_time * 2 * np.pi / (24*60)), np.cos(meal_time * 2 * np.pi / (24*60))])
time_encoding = pd.Series(time_encoding, index=["time_sin", "time_cos"])
df_features_train.append(pd.concat((pd.Series(user_id, index=["user_id"]), df_gluc_features, df_meals_features, time_encoding,
row[["fat_g", "carbohydrate_g", "protein_g", "fiber_g"]])))
df_glucose_train_out = pd.concat(df_glucose_train_list_out, axis=1).T
df_features_train = pd.concat(df_features_train, axis=1).T.set_index("user_id")
# extract features from test runs
df_glucose_test_list_out = []
df_features_test = []
for i, user_id in enumerate(df_final_test_meal["user_id"].unique()):
df_user_meals_test = df_meals_test[df_meals_test["user_id"] == user_id]
df_user_glucose_test = df_glucose_test[df_glucose_test["user_id"] == user_id]
# take glucose until 2/3 of the way through for features
df_gluc_features = df_user_glucose_test["bg"].quantile([0.1, 0.25, 0.5, 0.75, 0.9])
df_gluc_features = df_gluc_features.rename({0.1: "bg_10th", 0.25: "bg_25th", 0.5: "bg_50th", 0.75: "bg_75th", 0.9: "bg_90th"})
# use macronutrient averages as features up until 2/3 of the way through
df_meals_features = df_user_meals_test[["carbohydrate_g", "fat_g", "protein_g", "fiber_g"]].mean()
df_meals_features = df_meals_features.rename({"carbohydrate_g": "carbohydrate_g_mean", "fat_g": "fat_g_mean",
"protein_g": "protein_g_mean", "fiber_g": "fiber_g_mean"})
if df_meals_features.isna().any() or df_gluc_features.isna().any():
continue
user_meal = df_final_test_meal[df_final_test_meal["user_id"] == user_id]
meal_time = user_meal["start_time"].iloc[0]
df_glucose_meal_after = df_final_test_glucose[df_final_test_glucose["user_id"] == user_id]
if len(df_glucose_meal_after) >= time_points_needed_test:
df_glucose_test_list_out.append(df_glucose_meal_after["bg"].rename(user_id).reset_index(drop=True))
meal_time = meal_time.hour * 60 + meal_time.minute
time_encoding = np.array([np.sin(meal_time * 2 * np.pi / (24*60)), np.cos(meal_time * 2 * np.pi / (24*60))])
time_encoding = pd.Series(time_encoding, index=["time_sin", "time_cos"])
df_features_test.append(pd.concat((pd.Series(user_id, index=["user_id"]), df_gluc_features, df_meals_features, time_encoding,
user_meal.iloc[0][["fat_g", "carbohydrate_g", "protein_g", "fiber_g"]])))
df_glucose_test_out = pd.concat(df_glucose_test_list_out, axis=1).T
df_features_test = pd.concat(df_features_test, axis=1).T.set_index("user_id")
# train model
features_train = np.asarray(df_features_train).astype(np.float32)
glucose_train_out = np.asarray(df_glucose_train_out).astype(np.float32)
features_test = np.asarray(df_features_test).astype(np.float32)
glucose_test_out = np.asarray(df_glucose_test_out).astype(np.float32)
X_train, X_val, Y_train, Y_val = train_test_split(features_train, glucose_train_out,
test_size=0.2, random_state=0)
inputs = Input(shape=(X_train.shape[1]))
x = Dense(32, activation="relu")(inputs)
x = Dense(32, activation="relu")(x)
x = Dense(32, activation="relu")(x)
x = Dense(Y_train.shape[1])(x)
model = Model(inputs=[inputs], outputs=[x])
model.compile(
optimizer=Adam(learning_rate=0.0002),
loss="mse",
metrics=[tf.keras.metrics.MeanAbsoluteError(name="mean_absolute_error")],
)
val_baseline = model.evaluate(X_val, Y_val, verbose=0)[0]
cb_lr_reducer = ReduceLROnPlateau(monitor="val_loss", factor=0.5, patience=5, min_lr=1e-7)
cb_early_stopping = EarlyStopping(
monitor="val_loss",
min_delta=0.01,
patience=25,
verbose=0,
mode="auto",
baseline=val_baseline,
restore_best_weights=True,
)
model.fit(
X_train,
Y_train,
validation_data=(X_val, Y_val),
epochs=1000,
callbacks=[cb_lr_reducer, cb_early_stopping],
verbose=1,
)
test_loss, test_mae = model.evaluate(features_test, glucose_test_out, verbose=0)
print("Test Loss: ", test_loss)
print("Test MAE: ", test_mae)