Morten Hjorth-Jensen
76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
"""
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Code to test Ridge and NNs using Scikit-Learn only
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"""
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn import linear_model
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from sklearn.neural_network import MLPRegressor
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from sklearn.metrics import accuracy_score
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import seaborn as sns
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def MSE(y_data,y_model):
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n = np.size(y_model)
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return np.sum((y_data-y_model)**2)/n
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# A seed just to ensure that the random numbers are the same for every run.
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# Useful for eventual debugging.
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np.random.seed(315)
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n = 1000
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x = np.random.rand(n)
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y = x+x*x
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X = np.zeros((n,1))
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X[:,0] = x
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# We split the data in test and training data
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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# Decide which values of lambda to use
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nlambdas = 5
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lmbd_vals = np.logspace(-4, 0, nlambdas)
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MSERidgePredict = np.zeros(nlambdas)
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for i in range(nlambdas):
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lmb = lmbd_vals[i]
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RegRidge = linear_model.Ridge(lmb)
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RegRidge.fit(X_train,y_train)
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ypredictRidge = RegRidge.predict(X_test)
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MSERidgePredict[i] = MSE(y_test,ypredictRidge)
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plt.figure()
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plt.plot(np.log10(lmbd_vals), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')
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plt.xlabel('log10(lambda)')
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plt.ylabel('MSE')
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plt.legend()
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plt.show()
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# Neural Network part
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n_hidden_neurons = 100
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epochs = 100
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# store models for later use
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eta_vals = np.logspace(-4, 0, 5)
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# store the models for later use
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DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
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test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
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sns.set()
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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 = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
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alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
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dnn.fit(X_train, y_train)
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ypredictMLP = dnn.predict(X_test)
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test_accuracy[i][j] = MSE(ypredictMLP, y_test)
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fig, ax = plt.subplots(figsize = (10, 10))
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sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
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ax.set_title("Test Accuracy")
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ax.set_ylabel("$\eta$")
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ax.set_xlabel("$\lambda$")
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plt.show()
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