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feat: C++ kmeans
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straile
parent
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commit
51b3608b68
@ -1,21 +1,20 @@
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import tensorflow as tf
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import numpy as np
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from sklearn.cluster import KMeans
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import os
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def initiate_model(model_file_path, cuda_dir):
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os.environ["TF_XLA_FLAGS"] = "--tf_xla_cpu_global_jit"
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os.environ["XLA_FLAGS"] = "--xla_gpu_cuda_data_dir=" + cuda_dir
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os.environ["TF_XLA_FLAGS"] = "--tf_xla_cpu_global_jit"
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os.environ["XLA_FLAGS"] = "--xla_gpu_cuda_data_dir=" + cuda_dir
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def k_means(data, k=2, tol=1e-6):
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kmeans = KMeans(n_clusters=k, tol=tol)
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labels = kmeans.fit_predict(data)
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return labels
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def initiate_model(model_file_path):
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print("AI: Model loaded from: " + model_file_path, flush=True)
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model = tf.keras.models.load_model(model_file_path)
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return model
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def training_step(model, x, y, x_val, y_val, batch_size, epochs):
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history = model.fit(x, y,
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epochs=epochs,
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batch_size=batch_size,
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validation_data=(x_val, y_val))
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print(history, flush=True)
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return history["val_loss"]
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def prediction_step(model, x, batch_size):
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prediction = model.predict(x, batch_size)
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@ -27,6 +26,22 @@ def get_weights(model):
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return weights
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def training_step(model, x, y, batch_size, epochs, output_file_path):
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# Check clustering of input data
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# and only train for the cluster where nothing is happening
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labels = k_means(x)
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n = int(np.sqrt(len(labels)))
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for row in range(n):
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row_values = []
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for col in range(n):
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row_values.append(labels[((n - (row + 1)) * n) + col])
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print("".join(map(str, row_values)), flush=True)
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x = x[labels==labels[-1]]
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y = y[labels==labels[-1]]
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print("Relevant Cluster is: " + str(labels[-1]), flush=True)
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print("Data size is: " + str(len(x)), flush=True)
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history = model.fit(x, y,
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epochs=epochs,
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batch_size=batch_size)
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@ -35,4 +50,3 @@ def training_step(model, x, y, batch_size, epochs, output_file_path):
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output_file_path += ".keras"
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model.save(output_file_path)
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return history
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@ -21,11 +21,12 @@ namespace poet {
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* functions are defined
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* @return 0 if function was succesful
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*/
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int Python_Keras_setup(std::string functions_file_path) {
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int Python_Keras_setup(std::string functions_file_path, std::string cuda_src_dir) {
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// Initialize Python functions
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Py_Initialize();
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// Import numpy functions
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_import_array();
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PyRun_SimpleString(("cuda_dir = \"" + cuda_src_dir + "\"").c_str()) ;
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FILE* fp = fopen(functions_file_path.c_str(), "r");
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int py_functions_initialized = PyRun_SimpleFile(fp, functions_file_path.c_str());
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fclose(fp);
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@ -43,24 +44,142 @@ int Python_Keras_setup(std::string functions_file_path) {
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* a variable "model_file_path" in the R input script
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* @return 0 if function was succesful
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*/
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int Python_Keras_load_model(std::string model_file_path, std::string cuda_src_dir) {
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int Python_Keras_load_model(std::string model_reaction, std::string model_no_reaction) {
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// Acquire the Python GIL
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PyGILState_STATE gstate = PyGILState_Ensure();
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// Initialize Keras model
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int py_model_loaded = PyRun_SimpleString(("model = initiate_model(\"" +
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model_file_path + "\", \"" +
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cuda_src_dir + "\")").c_str());
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// Initialize Keras model for the reaction cluster
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int py_model_loaded = PyRun_SimpleString(("model_reaction = initiate_model(\"" +
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model_reaction + "\")").c_str());
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if (py_model_loaded != 0) {
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PyErr_Print(); // Ensure that python errors make it to stdout
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throw std::runtime_error("Keras model could not be loaded from: " + model_file_path);
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throw std::runtime_error("Keras model could not be loaded from: " + model_reaction);
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}
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// Initialize Keras model for the no reaction cluster
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py_model_loaded = PyRun_SimpleString(("model_no_reaction = initiate_model(\"" +
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model_no_reaction + "\")").c_str());
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if (py_model_loaded != 0) {
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PyErr_Print(); // Ensure that python errors make it to stdout
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throw std::runtime_error("Keras model could not be loaded from: " + model_no_reaction);
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}
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// Release the Python GIL
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PyGILState_Release(gstate);
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return py_model_loaded;
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}
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/**
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* @brief Calculates the euclidian distance between two points in n dimensional space
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* @param a Point a
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* @param b Point b
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* @return The distance
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*/
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double distance(const std::vector<double>& a, const std::vector<double>& b) {
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double sum = 0.0;
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for (size_t i = 0; i < a.size(); ++i) {
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sum += (a[i] - b[i]) * (a[i] - b[i]);
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}
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return sqrt(sum);
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}
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/**
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* @brief Assigns all elements of a 2D-Matrix to the nearest cluster center point
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* @param field 2D-Matrix with the content of a Field object
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* @param clusters The vector of clusters represented by their center points
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* @return A vector that contains the assigned cluster for each of the rows in field
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*/
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std::vector<int> assign_clusters(const std::vector<vector<double>>& field, const std::vector<vector<double>>& clusters) {
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// Initiate a vector that holds the cluster labels of each row
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std::vector<int> labels(field[0].size());
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for (size_t row = 0; row < labels.size(); row++) {
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// Get the coordinates of the current row
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std::vector<double> row_data(field.size());
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for (int column = 0; column < row_data.size(); column++) {
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row_data[column] = field[column][row];
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}
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// Iterate over the clusters and check which cluster center is the closest
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double current_min_distance = numeric_limits<double>::max();
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int current_closest_cluster;
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for (size_t cluster = 0; cluster < clusters.size(); cluster++) {
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double cluster_distance = distance(row_data, clusters[cluster]);
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if (cluster_distance < current_min_distance) {
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current_min_distance = cluster_distance;
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current_closest_cluster = cluster;
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}
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}
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labels[row] = current_closest_cluster;
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}
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return labels;
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}
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/**
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* @brief Calculates new center points for each given cluster by averaging the coordinates
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* of all points that are assigen to it
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* @param field 2D-Matrix with the content of a Field object
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* @param labels The vector that contains the assigned cluster for each of the rows in field
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* @param k The number of clusters
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* @return The new cluster center points
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*/
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std::vector<vector<double>> calculate_new_clusters(const std::vector<std::vector<double>>& field,
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const vector<int>& labels, int k) {
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int columns = field.size();
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int rows = field[0].size();
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std::vector<std::vector<double>> clusters(k, std::vector<double>(columns, 0.0));
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vector<int> count(k, 0);
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// Sum the coordinates of all points that are assigned to each cluster
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for (int row = 0; row < rows; row++) {
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int assigned_cluster = labels[row];
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for (int column = 0; column < columns; column++) {
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clusters[assigned_cluster][column] += field[column][row];
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}
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count[assigned_cluster]++;
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}
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// Take the average of the summed coordinates
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for (int cluster = 0; cluster < k; cluster++) {
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if (count[cluster] == 0) continue;
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for (int column = 0; column < columns; column++) {
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clusters[cluster][column] /= count[cluster];
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}
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}
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return clusters;
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}
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/**
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* @brief Performs KMeans clustering for the elements of a 2D-Matrix
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* @param field 2D-Matrix with the content of a Field object
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* @param k The number of different clusters
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* @param iterations The number of cluster update steps
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* @return A vector that contains the assigned cluster for each of the rows in field
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*/
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std::vector<int> kMeans(std::vector<std::vector<double>>& field, int k, int iterations) {
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// Initialize cluster centers by selecting random points from the field
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srand(time(0));
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std::vector<vector<double>> clusters;
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for (int i = 0; i < k; ++i) {
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std::vector<double> cluster_center(field.size());
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int row = rand() % field.size();
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for (int column = 0; column < cluster_center.size(); column++) {
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cluster_center[column] = field[column][row];
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}
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clusters.push_back(cluster_center);
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}
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std::vector<int> labels;
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for (int iter = 0; iter < iterations; ++iter) {
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// Get the nearest cluster for each row
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labels = assign_clusters(field, clusters);
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// Update each cluster center as the average location of each point assigned to it
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std::vector<vector<double>> new_clusters = calculate_new_clusters(field, labels, k);
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clusters = new_clusters;
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}
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return labels;
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}
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/**
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* @brief Converts the std::vector 2D matrix representation of a POET Field object to a numpy array
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* for use in the Python AI surrogate functions
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@ -120,7 +239,7 @@ std::vector<double> numpy_array_to_vector(PyObject* py_array) {
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* @return Predictions that the neural network made from the input values x. The predictions are
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* represented as a vector similar to the representation from the Field.AsVector() method
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*/
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std::vector<double> Python_Keras_predict(std::vector<std::vector<double>> x, int batch_size) {
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std::vector<double> Python_Keras_predict(std::vector<std::vector<double>>& x, int batch_size) {
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// Acquire the Python GIL
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PyGILState_STATE gstate = PyGILState_Ensure();
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@ -130,7 +249,7 @@ std::vector<double> Python_Keras_predict(std::vector<std::vector<double>> x, int
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// Get the model and training function from the global python interpreter
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PyObject* py_main_module = PyImport_AddModule("__main__");
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PyObject* py_global_dict = PyModule_GetDict(py_main_module);
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PyObject* py_keras_model = PyDict_GetItemString(py_global_dict, "model");
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PyObject* py_keras_model = PyDict_GetItemString(py_global_dict, "model_no_reaction");
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PyObject* py_inference_function = PyDict_GetItemString(py_global_dict, "prediction_step");
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// Build the function arguments as four python objects and an integer
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PyObject* args = Py_BuildValue("(OOi)",
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@ -184,7 +303,7 @@ Eigen::MatrixXd eigen_inference_batched(const Eigen::Ref<const Eigen::MatrixXd>&
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* @return Predictions that the neural network made from the input values x. The predictions are
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* represented as a vector similar to the representation from the Field.AsVector() method
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*/
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std::vector<double> Eigen_predict(const EigenModel& model, std::vector<std::vector<double>> x, int batch_size,
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std::vector<double> Eigen_predict(const EigenModel& model, std::vector<std::vector<double>>& x, int batch_size,
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std::mutex* Eigen_model_mutex) {
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// Convert input data to Eigen matrix
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const int num_samples = x[0].size();
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@ -249,7 +368,7 @@ void training_data_buffer_append(std::vector<std::vector<double>>& training_data
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* @param y Training data targets
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* @param params Global runtime paramters
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*/
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void Python_keras_train(std::vector<std::vector<double>> x, std::vector<std::vector<double>> y,
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void Python_Keras_train(std::vector<std::vector<double>>& x, std::vector<std::vector<double>>& y,
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const RuntimeParameters& params) {
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// Prepare data for python
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PyObject* py_df_x = vector_to_numpy_array(x);
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@ -258,7 +377,7 @@ void Python_keras_train(std::vector<std::vector<double>> x, std::vector<std::vec
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// Get the model and training function from the global python interpreter
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PyObject* py_main_module = PyImport_AddModule("__main__");
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PyObject* py_global_dict = PyModule_GetDict(py_main_module);
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PyObject* py_keras_model = PyDict_GetItemString(py_global_dict, "model");
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PyObject* py_keras_model = PyDict_GetItemString(py_global_dict, "model_no_reaction");
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PyObject* py_training_function = PyDict_GetItemString(py_global_dict, "training_step");
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// Build the function arguments as four python objects and an integer
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@ -350,7 +469,7 @@ void parallel_training(EigenModel* Eigen_model,
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PyGILState_STATE gstate = PyGILState_Ensure();
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// Start training
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Python_keras_train(inputs, targets, params);
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Python_Keras_train(inputs, targets, params);
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if (!params.use_Keras_predictions) {
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std::cout << "AI: Training thread: Update shared model weights" << std::endl;
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@ -435,7 +554,7 @@ std::vector<std::vector<std::vector<double>>> Python_Keras_get_weights() {
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PyObject* py_main_module = PyImport_AddModule("__main__");
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PyObject* py_global_dict = PyModule_GetDict(py_main_module);
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PyObject* py_keras_model = PyDict_GetItemString(py_global_dict, "model");
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PyObject* py_keras_model = PyDict_GetItemString(py_global_dict, "model_no_reaction");
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PyObject* py_get_weights_function = PyDict_GetItemString(py_global_dict, "get_weights");
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PyObject* args = Py_BuildValue("(O)", py_keras_model);
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@ -40,14 +40,16 @@ struct TrainingData {
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// Ony declare the actual functions if flag is set
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#ifdef USE_AI_SURROGATE
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int Python_Keras_setup(std::string functions_file_path);
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int Python_Keras_setup(std::string functions_file_path, std::string cuda_src_dir);
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void Python_finalize(std::mutex* Eigen_model_mutex, std::mutex* training_data_buffer_mutex,
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std::condition_variable* training_data_buffer_full, bool* start_training, bool* end_training);
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int Python_Keras_load_model(std::string model_file_path, std::string cuda_src_dir);
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int Python_Keras_load_model(std::string model_reaction, std::string model_no_reaction);
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std::vector<double> Python_Keras_predict(std::vector<std::vector<double>> x, int batch_size);
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std::vector<int> kMeans(std::vector<std::vector<double>>& field, int k, int maxIterations = 100);
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std::vector<double> Python_Keras_predict(std::vector<std::vector<double>>& x, int batch_size);
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void training_data_buffer_append(std::vector<std::vector<double>>& training_data_buffer,
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std::vector<std::vector<double>>& new_values);
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@ -64,16 +66,17 @@ void update_weights(EigenModel* model, const std::vector<std::vector<std::vector
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std::vector<std::vector<std::vector<double>>> Python_Keras_get_weights();
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std::vector<double> Eigen_predict(const EigenModel& model, std::vector<std::vector<double>> x, int batch_size,
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std::vector<double> Eigen_predict(const EigenModel& model, std::vector<std::vector<double>>& x, int batch_size,
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std::mutex* Eigen_model_mutex);
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// Otherwise, define the necessary stubs
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#else
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inline void Python_Keras_setup(std::string){}
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inline void Python_Keras_setup(std::string, std::string){}
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inline void Python_finalize(std::mutex*, std::mutex*, std::condition_variable*, bool*, bool*){}
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inline void Python_Keras_load_model(std::string, std::string){}
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inline std::vector<int> kMeans(std::vector<std::vector<double>>&, int, int) {return {};}
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inline std::vector<double> Python_Keras_predict(std::vector<std::vector<double>>&, int){return {};}
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inline void training_data_buffer_append(std::vector<std::vector<double>>&, std::vector<std::vector<double>>&){}
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inline std::vector<double> Python_Keras_predict(std::vector<std::vector<double>>, int){return {};}
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inline int Python_Keras_training_thread(EigenModel*, std::mutex*,
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TrainingData*, std::mutex*,
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std::condition_variable*,
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@ -81,7 +84,7 @@ inline int Python_Keras_training_thread(EigenModel*, std::mutex*,
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inline void update_weights(EigenModel*, const std::vector<std::vector<std::vector<double>>>&){}
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inline std::vector<std::vector<std::vector<double>>> Python_Keras_get_weights(){return {};}
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inline std::vector<double> Eigen_predict(const EigenModel&, std::vector<std::vector<double>>, int, std::mutex*){return {};}
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inline std::vector<double> Eigen_predict(const EigenModel&, std::vector<std::vector<double>>&, int, std::mutex*){return {};}
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#endif
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} // namespace poet
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30
src/poet.cpp
30
src/poet.cpp
@ -297,7 +297,9 @@ static Rcpp::List RunMasterLoop(RInsidePOET &R, const RuntimeParameters ¶ms,
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TrainingData training_data_buffer;
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if (params.use_ai_surrogate) {
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MSG("AI: Initialize model");
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Python_Keras_load_model(R["model_file_path"], params.cuda_src_dir);
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// Initiate two models from one file TODO: Expand this for two input files
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Python_Keras_load_model(R["model_file_path"], R["model_file_path"]);
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if (!params.disable_training) {
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MSG("AI: Initialize training thread");
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Python_Keras_training_thread(&Eigen_model, &Eigen_model_mutex,
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@ -352,6 +354,7 @@ static Rcpp::List RunMasterLoop(RInsidePOET &R, const RuntimeParameters ¶ms,
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if (params.use_ai_surrogate) {
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double ai_start_t = MPI_Wtime();
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// Get current values from the tug field for the ai predictions
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R["TMP"] = Rcpp::wrap(chem.getField().AsVector());
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R.parseEval(std::string("predictors <- ") +
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@ -360,13 +363,32 @@ static Rcpp::List RunMasterLoop(RInsidePOET &R, const RuntimeParameters ¶ms,
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// Apply preprocessing
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MSG("AI Preprocessing");
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R.parseEval("predictors_scaled <- preprocess(predictors)");
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std::vector<std::vector<double>> predictors_scaled = R["predictors_scaled"];
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// get k means
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MSG("KMEANSSSSS:");
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std::vector<int> labels = kMeans(predictors_scaled, 2, 300);
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int size = (int)(std::sqrt(chem.getField().GetRequestedVecSize()));
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MSG("SIZE: " + std::to_string(size));
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for (int row = size; row > 0; row--) {
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for (int column = 0; column < size; column++) {
|
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std::cout << labels[((row - 1) * size) + column];
|
||||
}
|
||||
std::cout << std::endl;
|
||||
}
|
||||
|
||||
|
||||
MSG("AI: Predict");
|
||||
if (params.use_Keras_predictions) { // Predict with Keras default function
|
||||
R["TMP"] = Python_Keras_predict(R["predictors_scaled"], params.batch_size);
|
||||
R["TMP"] = Python_Keras_predict(predictors_scaled, params.batch_size);
|
||||
|
||||
} else { // Predict with custom Eigen function
|
||||
R["TMP"] = Eigen_predict(Eigen_model, R["predictors_scaled"], params.batch_size, &Eigen_model_mutex);
|
||||
R["TMP"] = Eigen_predict(Eigen_model, predictors_scaled, params.batch_size, &Eigen_model_mutex);
|
||||
}
|
||||
|
||||
// Apply postprocessing
|
||||
@ -666,7 +688,7 @@ int main(int argc, char *argv[]) {
|
||||
MSG("AI: Initialize Python for AI surrogate functions");
|
||||
std::string python_keras_file = std::string(SRC_DIR) +
|
||||
"/src/Chemistry/SurrogateModels/AI_Python_functions/keras_AI_surrogate.py";
|
||||
Python_Keras_setup(python_keras_file);
|
||||
Python_Keras_setup(python_keras_file, run_params.cuda_src_dir);
|
||||
}
|
||||
|
||||
MSG("Init done on process with rank " + std::to_string(MY_RANK));
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user