Adding files

mpq/uci_fann_net/uci_fann_net.py

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+import init_utils
+import common
+
+# Initialize the environment and get the name
+name = init_utils.initialize_environment(__file__)
+args = init_utils.get_args()
+
+# Set arguments from command line
+max_acc_drop = args.max_acc_drop
+device = args.device
+
+import pandas as pd 
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing 
+from sklearn.preprocessing import MinMaxScaler
+
+# Load our Dataset
+df = pd.read_csv("uci_fann_net/Data_Cortex_Nuclear.csv")
+# Fix the Missing values
+
+missing = df.isnull().sum().sum()
+print("There are total {} null values".format(missing) )
+
+missval_col = df.columns[df.isna().any()].tolist()
+
+# Imputing Missing values with mean values 
+for i in range(len(missval_col)):
+    mean_value = np.mean(df[missval_col[i]])
+    #df[missval_col[i]].fillna(value = mean_value, inplace = True)
+    df.fillna({missval_col[i] : mean_value}, inplace = True)
+
+print("Total number of Null Value in the updated dataset is {}".format(df.isnull().sum().sum()))
+
+numcol = df.describe().columns.tolist()
+X = df[numcol] # X dataset should include cols with numerical datasets, the protein expression level
+
+r, c = df.shape
+r2, c2 = X.shape
+
+# Preprocess the Data with the MinMaxScaler
+
+scaler = MinMaxScaler(feature_range = (0, 255))
+df_normalized = pd.DataFrame(scaler.fit_transform(X), columns = X.columns)
+
+# Convert the normalized values to integers
+df_normalized = df_normalized.round()/255.0
+df_normalized = df_normalized.drop(columns = 'pAKT_N')
+
+label_encoder = preprocessing.LabelEncoder()
+
+X_data = df_normalized.to_numpy()
+y_data = label_encoder.fit_transform(df['class'])
+
+X_train, X_val, y_train, y_val = train_test_split(X_data, y_data, test_size = 0.3)
+X_test, X_val, y_test, y_val = train_test_split(X_val, y_val, test_size = 0.5)
+
+### The following variables could be set as inputs 
+
+BATCH_SIZE = 16
+epochs = [80, 20]
+lr = [0.0001, 0.00005]
+
+class FANN_NET(nn.Module):
+    def __init__(self):
+        super(FANN_NET, self).__init__()        
+        self.fc1 = nn.Linear(76, 300)
+        self.relu1 = nn.ReLU()
+        self.fc2 = nn.Linear(300, 200)
+        self.relu2 = nn.ReLU()
+        self.fc3 = nn.Linear(200, 100)
+        self.relu3 = nn.ReLU()
+        self.fc4 = nn.Linear(100, 10)
+        
+    def forward(self,X):
+        X = self.relu1(self.fc1(X))
+        X = self.relu2(self.fc2(X))
+        X = self.relu3(self.fc3(X))
+        X = self.fc4(X)
+        return F.log_softmax(X, dim = 1)
+
+net = FANN_NET()
+
+common.create_ibex_qnn(net, name, device, X_train, y_train, X_test, y_test, 
+                X_val = X_val, y_val = y_val, BATCH_SIZE = BATCH_SIZE, 
+                epochs = epochs, lr = lr, max_acc_drop = max_acc_drop)