added codes

2020-05-29 11:39:02 +02:00
parent a341783e27
commit 67ade4e63c
18 changed files with 76040 additions and 43 deletions
@@ -0,0 +1,95 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import warnings
+import itertools
+from PIL import Image
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,roc_curve,auc
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score
+warnings.filterwarnings("ignore")
+
+# Reading data using PANDA 
+data = pd.read_csv("pulsar_stars.csv")
+data.head()
+#DATA
+targets = data["target_class"]
+features = data.drop("target_class", axis = 1)
+np.random.seed(2018)
+#Split data
+X_train, X_test, y_train, y_test = train_test_split(features, targets, test_size = 0.2, random_state = 66)
+
+
+# Define the learning rate, hyperparameter using NUMPY 
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
+n_hidden_neurons = 50
+epochs = 100
+
+# Use scikit learn for neural network 
+DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        dnn = MLPClassifier(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
+                            alpha=lmbd, learning_rate_init=eta, max_iter=epochs, solver='adam')
+        dnn.fit(X_train, y_train)
+        DNN_scikit[i][j] = dnn
+        print("Learning rate  = ", eta)
+        print("Lambda = ", lmbd)
+        print("Accuracy score on test set: ", dnn.score(X_test, y_test))
+        print()
+
+        
+#Plot the accuracy as function of learning rate and hyperparameter        
+sns.set() 
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+
+for i in range(len(eta_vals)):
+    for j in range(len(lmbd_vals)):
+        dnn = DNN_scikit[i][j]
+        train_pred = dnn.predict(X_train) 
+        test_pred = dnn.predict(X_test)
+        train_accuracy[i][j] = accuracy_score(y_train, train_pred)
+        test_accuracy[i][j] = accuracy_score(y_test, test_pred)
+        
+fig, ax = plt.subplots(figsize = (10, 10))        
+sns.heatmap(train_accuracy, annot=True,annot_kws={"size": 18}, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy",fontsize=18)
+ax.set_ylabel("$\eta$",fontsize=18)
+ax.set_yticklabels(eta_vals)
+ax.set_xlabel("$\lambda$",fontsize=18)
+ax.set_xticklabels(lmbd_vals)
+plt.tick_params(labelsize=18)
+ 
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True,annot_kws={"size": 18}, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy",fontsize=18)
+ax.set_ylabel("$\eta$",fontsize=18)
+ax.set_yticklabels(eta_vals)
+ax.set_xlabel("$\lambda$",fontsize=18)
+ax.set_xticklabels(lmbd_vals)
+plt.tick_params(labelsize=18)
+#plt.show()        
+
+#Plot confusion matrix at optimal values of learning rate and hyperameter
+dnn = MLPClassifier(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
+
+                            alpha=0.001, learning_rate_init=0.001, max_iter=epochs, solver='adam')
+dnn.fit(X_train,y_train)
+y_pred=dnn.predict(X_test)
+fig1, ax = plt.subplots(figsize = (13,10))
+sns.heatmap(confusion_matrix(y_test,y_pred),annot=True,fmt = "d",linecolor="k",linewidths=3)
+ax.set_xlabel('True label',fontsize=18)
+ax.set_ylabel('Predicted label',fontsize=18)
+ax.set_title("CONFUSION MATRIX",fontsize=20)
+plt.tick_params(labelsize=18)
+plt.show()
+
+# Feature importance -->weights
+coef=dnn.coefs_[0]
+print (coef)
+