newer updates

demo/another_demo.py

@@ -0,0 +1,58 @@
+import sys
+sys.path.append("../code")  # assuming "code" is one directory up and then down into "code"
+
+from DemoDataBuilderXandY import generate_dummy_data
+from Netrem_model_builder import netrem, netremCV
+import PriorGraphNetwork as graph
+import error_metrics as em 
+import essential_functions as ef
+import netrem_evaluation_functions as nm_eval
+import Netrem_model_builder as nm
+
+dummy_data = generate_dummy_data(corrVals = [0.9, 0.5, 0.1, -0.2, -0.8, -0.3],
+                    standardize_X = False,
+                    center_y = False, 
+                   num_samples_M = 100,
+                   train_data_percent = 70)
+
+X_df = dummy_data.X_df
+X_df.head()
+
+y_df = dummy_data.y_df
+y_df.head()
+
+# 70 samples for training data (used to train and fit GRegulNet model)
+X_train = dummy_data.view_X_train_df()
+y_train = dummy_data.view_y_train_df()
+
+# 30 samples for testing data
+X_test = dummy_data.view_X_test_df()
+y_test = dummy_data.view_y_test_df()
+
+# prior network edge_list:
+edge_list = [["TF1", "TF2", 0.9], ["TF4", "TF5", 0.75], ["TF1", "TF3"], ["TF1", "TF4"], ["TF1", "TF5"], 
+              ["TF2", "TF3"], ["TF2", "TF4"], ["TF2", "TF5"], ["TF3", "TF4"], ["TF3", "TF5"]]
+
+beta_network_val = 3 
+# by default, cv_for_alpha is False, so alpha_lasso_val will be specified for the alpha_lasso parameter.
+alpha_lasso_val = 0.01
+
+# Building the network regularized regression model: 
+# Please note: To include nodes found in the gene expression data that are not found in the PPI Network (e.g. TF6 in our case), we use False for the overlapped_nodes_only argument (otherwise, we would only use TFs 1 to 5):
+netrem_demo = nm.netrem(edge_list = edge_list, 
+                        beta_net = beta_network_val,
+                        alpha_lasso = alpha_lasso_val,
+                        overlapped_nodes_only = False, # so we include TF6
+                        view_network = True)
+
+# Fitting the gregulnet model on training data: X_train and y_train:
+netrem_demo.fit(X_train, y_train)
+
+pred_y_test = netrem_demo.predict(X_test) # predicted values for y_test
+mse_test = netrem_demo.test_mse(X_test, y_test)
+print(f"Please note that the testing Mean Square Error (MSE) is {mse_test}")
+
+# To view and extract the predicted model coefficients for the predictors: 
+netrem_demo.model_coef_df
+
+netrem_demo.B_interaction_df

demo/demo_toy.py

@@ -9,45 +9,47 @@
 import netrem_evaluation_functions as nm_eval
 import Netrem_model_builder as nm
 
-dummy_data = generate_dummy_data(corrVals = [0.9, 0.5, 0.1, -0.2, -0.8, -0.3],
-                    standardize_X = False,
-                    center_y = False, 
-                   num_samples_M = 100,
-                   train_data_percent = 70)
+dummy_data = generate_dummy_data(corrVals = [0.9, 0.5, 0.4, -0.3, -0.8], # the # of elements in corrVals is the # of predictors (X)
+                                 num_samples_M = 100000, # the number of samples M
+                                 train_data_percent = 70) # the remainder out of 100,000 will be kept for testing. If 100, then ALL data is used for training and testing.
 
 X_df = dummy_data.X_df
 X_df.head()
 
 y_df = dummy_data.y_df
 y_df.head()
 
-# 70 samples for training data (used to train and fit GRegulNet model)
+# 70,000 samples for training data (used to train and fit GRegulNet model)
 X_train = dummy_data.view_X_train_df()
 y_train = dummy_data.view_y_train_df()
 
-# 30 samples for testing data
+# 30,000 samples for testing data
 X_test = dummy_data.view_X_test_df()
 y_test = dummy_data.view_y_test_df()
 
-# prior network edge_list:
-edge_list = [["TF1", "TF2", 0.9], ["TF4", "TF5", 0.75], ["TF1", "TF3"], ["TF1", "TF4"], ["TF1", "TF5"], 
-              ["TF2", "TF3"], ["TF2", "TF4"], ["TF2", "TF5"], ["TF3", "TF4"], ["TF3", "TF5"]]
+X_train.corr() # pairwise correlations among the training samples
+X_test.corr() # pairwise correlations among the training samples
 
-beta_network_val = 3 
-# by default, cv_for_alpha is False, so alpha_lasso_val will be specified for the alpha_lasso parameter.
-alpha_lasso_val = 0.01
+
+# prior network edge_list (missing edges or edges with no edge weight will be added with the default_edge_list so the network is fully-connected):
+edge_list = [["TF1", "TF2", 0.8], ["TF4", "TF5", 0.95], ["TF1", "TF3"], ["TF1", "TF4"], ["TF1", "TF5"], 
+             ["TF2", "TF3"], ["TF2", "TF4"], ["TF2", "TF5"], ["TF3", "TF4"], ["TF3", "TF5"]]
+
+beta_network_val = 1 
+# by default, model_type is Lasso, so alpha_lasso_val will be specified for the alpha_lasso parameter. 
+# However, we will specify model_type = LassoCV, so our alpha_lasso is determined by cross-validation on training data).
 
 # Building the network regularized regression model: 
-# Please note: To include nodes found in the gene expression data that are not found in the PPI Network (e.g. TF6 in our case), we use False for the overlapped_nodes_only argument (otherwise, we would only use TFs 1 to 5):
-netrem_demo = nm.netrem(edge_list = edge_list, 
-                        beta_net = beta_network_val,
-                        alpha_lasso = alpha_lasso_val,
-                        overlapped_nodes_only = False, # so we include TF6
-                        view_network = True)
-
-# Fitting the gregulnet model on training data: X_train and y_train:
+# By default, edges are constructed between all of the nodes; nodes with a missing edge are assigned the default_edge_weight. 
+netrem_demo = netrem(edge_list = edge_list, 
+                     beta_net = beta_network_val,
+                     model_type = "LassoCV",
+                     view_network = True)
+
+# Fitting the NetREm model on training data: X_train and y_train:
 netrem_demo.fit(X_train, y_train)
 
+
 pred_y_test = netrem_demo.predict(X_test) # predicted values for y_test
 mse_test = netrem_demo.test_mse(X_test, y_test)
 print(f"Please note that the testing Mean Square Error (MSE) is {mse_test}")
@@ -56,3 +58,8 @@
 netrem_demo.model_coef_df
 
 netrem_demo.B_interaction_df
+
+
+netrem_demo.final_corr_vs_coef_df
+netrem_demo.combined_df
+organize_B_interaction_network(netrem_demo)