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