File size: 3,191 Bytes
b903156 | 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 | #this script with assistance from ChatGPT
# a spreadsheet is where the first row is a header and first column is participant id
# the goal is to predict the column "wab_aq" based on imaging data
# we treat "age_at_stroke" as a predictor
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import LeaveOneOut
from sklearn.metrics import mean_squared_error
from scipy.stats import pearsonr
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from sklearn.svm import SVR
#required: column name for response variable
rv = 'wab_aq'
#optional: remove specific features
columns_to_drop = []
# columns_to_drop = ['age_at_stroke', 'lesion_volume']
#optional: only preserve specific features (make sure to keep rv)
columns_to_keep = []
# columns_to_keep = ['age_at_stroke', 'lesion_volume', rv]
#required: name of spreadsheet to analyze
file_path = 'merged_artery_participants.tsv' # Replace with the path to your data file
# 1. Import data from Excel
# n.b. ignore the first column (particpant_id)
data = pd.read_csv(file_path, sep='\t', index_col=0)
# print(data)
# Remove rows where the rv has NaN values
data = data.dropna(subset=[rv])
if bool(columns_to_keep):
data = data[columns_to_keep]
if bool(columns_to_drop):
data = data.drop(columns_to_drop, axis=1)
# 3. Replace NaN with zeros and remove sparse columns
data = data.fillna(0)
data = data.loc[:, (data != 0).sum() > 0.1 * data.shape[0]]
# 4. Prepare data for leave-one-out
X = data.drop([rv], axis=1)
y = data[rv]
#optional normalize rv in range of 0..1, but based on whole sample leakage for leave-one-out
#y = (y - y.min()) / (y.max() - y.min())
scaler = StandardScaler()
# 5. Initialize for leave-one-out cross-validation
loo = LeaveOneOut()
y_true, y_pred_nn, y_pred_svr = [], [], []
for train_index, test_index in loo.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
# store m/std dev of training data and apply normalization to train and test data separately
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Neural Network
model_nn = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1)
])
model_nn.compile(optimizer='adam', loss='mean_squared_error')
model_nn.fit(X_train, y_train, epochs=50, batch_size=10, verbose=0)
pred_nn = model_nn.predict(X_test).flatten()
# SVR
model_svr = SVR(kernel='rbf')
model_svr.fit(X_train, y_train)
pred_svr = model_svr.predict(X_test)
# Collecting predictions
y_true.extend(y_test)
y_pred_nn.extend(pred_nn)
y_pred_svr.extend(pred_svr)
# 6. Calculate the correlation (R) and p-value for both models
correlation_nn, p_value_nn = pearsonr(y_true, y_pred_nn)
correlation_svr, p_value_svr = pearsonr(y_true, y_pred_svr)
print(f'Neural Network - Correlation (R): {correlation_nn}, p-value: {p_value_nn}')
print(f'SVR - Correlation (R): {correlation_svr}, p-value: {p_value_svr}') |