You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
433 lines
112 KiB
Plaintext
433 lines
112 KiB
Plaintext
7 months ago
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"id": "f75621c4-16cd-4320-a777-2fadf7231c9f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"from sklearn.linear_model import LinearRegression\n",
|
||
|
|
"from sklearn.preprocessing import PolynomialFeatures"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"id": "a4f8dcc4-d866-442e-859d-7895e49acde8",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#Load the training and test data\n",
|
||
|
|
"X_train=np.load('/home/unipi/v.vichi3/Desktop/X_train.npy')\n",
|
||
|
|
"X_test=np.load('/home/unipi/v.vichi3/Desktop/X_test.npy')\n",
|
||
|
|
"y_train=np.load('/home/unipi/v.vichi3/Desktop/y_train.npy')\n",
|
||
|
|
"y_test=np.load('/home/unipi/v.vichi3/Desktop/y_test.npy')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"id": "c1048709-1a49-4737-b1cd-7dd67033623d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(600000, 5) (52376, 5) (600000,) (52376,)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"id": "0edb7d7f-e148-4612-9c7c-97c572e3cb4e",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Mean of the MOID over the training set: 0.18033655783333338\n",
|
||
|
|
"MSE for test data: 0.016517902853446634\n",
|
||
|
|
"MAE for test data: 0.1054009483071237\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#The baseline model is the one that computes the mean of the target variable on the training set and returns, for \n",
|
||
|
|
"#all instances in the test set, that mean\n",
|
||
|
|
"y_bar=y_train.mean()\n",
|
||
|
|
"print(\"Mean of the MOID over the training set:\", y_bar)\n",
|
||
|
|
"#Compute the MSE and MAE\n",
|
||
|
|
"MSE=0\n",
|
||
|
|
"MAE=0\n",
|
||
|
|
"for i in range(len(y_test)):\n",
|
||
|
|
" MSE += (y_test[i]-y_bar)**2\n",
|
||
|
|
" MAE += abs(y_test[i]-y_bar)\n",
|
||
|
|
"MAE=MAE/len(y_test)\n",
|
||
|
|
"MSE=MSE/len(y_test)\n",
|
||
|
|
"print(\"MSE for test data:\", MSE)\n",
|
||
|
|
"print(\"MAE for test data:\", MAE)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"id": "f4d08069-349a-4665-99e1-96b37e00c60e",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(y_test[:100],'bo',markersize=3)\n",
|
||
|
|
"plt.plot(y_bar*np.ones((100,)), 'r*',markersize=3)\n",
|
||
|
|
"plt.legend(['true','predicted'],loc='upper right')\n",
|
||
|
|
"plt.title('Baseline model')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"id": "e8e12366-4e6e-412a-ab2e-6bc9a40eccd4",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"R squared on test set: 0.1100957393030294\n",
|
||
|
|
"Intercept: 0.11164309822134315\n",
|
||
|
|
"Coefficients: [ 4.82630044e-02 -1.51712311e-01 1.93476860e-03 1.55072413e-06\n",
|
||
|
|
" 3.06501066e-06]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#Linear Regression\n",
|
||
|
|
"lin_reg=LinearRegression().fit(X_train,y_train)\n",
|
||
|
|
"r_sq=lin_reg.score(X_test,y_test)\n",
|
||
|
|
"print(\"R squared on test set:\", r_sq)\n",
|
||
|
|
"print(\"Intercept:\", lin_reg.intercept_)\n",
|
||
|
|
"print(\"Coefficients:\", lin_reg.coef_)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"id": "62de0aae-4bbf-4659-a0fe-0822fd297dbd",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"y_pred=lin_reg.intercept_+np.sum(lin_reg.coef_*X_test,axis=1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"id": "e040808e-1800-4976-923f-d1f0d8de2db3",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"MSE for test data: 0.014699348657340645\n",
|
||
|
|
"MAE for test data: 0.09811802278034978\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"MSE=0\n",
|
||
|
|
"MAE=0\n",
|
||
|
|
"for i in range(len(y_test)):\n",
|
||
|
|
" MSE += (y_test[i]-y_pred[i])**2\n",
|
||
|
|
" MAE += abs(y_test[i]-y_pred[i])\n",
|
||
|
|
"MAE=MAE/len(y_test)\n",
|
||
|
|
"MSE=MSE/len(y_test)\n",
|
||
|
|
"print(\"MSE for test data:\", MSE)\n",
|
||
|
|
"print(\"MAE for test data:\", MAE)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"id": "bbb18511-4532-432f-ada9-6eefd790b0a5",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(y_test[:100],'bo',markersize=3)\n",
|
||
|
|
"plt.plot(y_pred[:100], 'r*',markersize=3)\n",
|
||
|
|
"plt.legend(['true','predicted'],loc='upper right')\n",
|
||
|
|
"plt.title('Linear Regression')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"id": "ad14da90-4d4b-4c7d-b2e1-1079a72b1dbd",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#Polynomial Regression, d=2\n",
|
||
|
|
"X_train2=PolynomialFeatures(degree=2,include_bias=False).fit_transform(X_train)\n",
|
||
|
|
"X_test2=PolynomialFeatures(degree=2,include_bias=False).fit_transform(X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"id": "e53b6e8d-688d-4672-9144-ce708f0e4aaf",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"R squared on test set: 0.27590125893584405\n",
|
||
|
|
"Number of parameters: 21\n",
|
||
|
|
"Intercept: -0.06682331717498041\n",
|
||
|
|
"Coefficients: [ 2.64761936e-01 -3.68484985e-01 1.98222192e-03 3.49081805e-08\n",
|
||
|
|
" -2.11007545e-05 5.18185781e-02 -6.50491717e-01 -8.39355649e-04\n",
|
||
|
|
" 3.53009628e-06 -1.87802540e-06 1.04755036e+00 8.00918631e-03\n",
|
||
|
|
" -1.17843565e-05 1.49267806e-05 -3.71225082e-05 -7.21465016e-08\n",
|
||
|
|
" -2.60640620e-08 9.40777227e-10 1.35524373e-08 4.81669732e-08]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pol_reg2=LinearRegression().fit(X_train2,y_train)\n",
|
||
|
|
"print(\"R squared on test set:\", pol_reg2.score(X_test2,y_test))\n",
|
||
|
|
"print(\"Number of parameters:\", len(pol_reg2.coef_)+1)\n",
|
||
|
|
"print(\"Intercept:\", pol_reg2.intercept_)\n",
|
||
|
|
"print(\"Coefficients:\", pol_reg2.coef_)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"id": "6ef45f92-8495-45ef-8d16-ebfed26cd02e",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"y_pred2=pol_reg2.predict(X_test2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"id": "c3710a93-8fe2-4337-b680-4b560bd69cd5",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"MSE for test data: 0.011960589837953305\n",
|
||
|
|
"MAE for test data: 0.08473643514764896\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"MSE=0\n",
|
||
|
|
"MAE=0\n",
|
||
|
|
"for i in range(len(y_test)):\n",
|
||
|
|
" MSE += (y_test[i]-y_pred2[i])**2\n",
|
||
|
|
" MAE += abs(y_test[i]-y_pred2[i])\n",
|
||
|
|
"MAE=MAE/len(y_test)\n",
|
||
|
|
"MSE=MSE/len(y_test)\n",
|
||
|
|
"print(\"MSE for test data:\", MSE)\n",
|
||
|
|
"print(\"MAE for test data:\", MAE)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"id": "38dc8afc-a400-43b6-965b-b2da89e2ba2c",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(y_test[:100],'bo',markersize=3)\n",
|
||
|
|
"plt.plot(y_pred2[:100], 'r*',markersize=3)\n",
|
||
|
|
"plt.legend(['true','predicted'],loc='upper right')\n",
|
||
|
|
"plt.title('Polynomial Regression, d=2')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"id": "ca6fc0f5-e6f0-4d55-a2ba-40acdbbf4151",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#Polynomial Regression, d=3\n",
|
||
|
|
"X_train3=PolynomialFeatures(degree=3,include_bias=False).fit_transform(X_train)\n",
|
||
|
|
"X_test3=PolynomialFeatures(degree=3,include_bias=False).fit_transform(X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"id": "acbe7bca-3b74-4099-acbe-e1349f988f50",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"R squared on test set: 0.408933403538129\n",
|
||
|
|
"Number of parameters: 56\n",
|
||
|
|
"Intercept: -0.0642778564287125\n",
|
||
|
|
"Coefficients: [-4.85030431e-01 2.35254231e+00 2.56156230e-03 -1.73008136e-04\n",
|
||
|
|
" 1.36437760e-04 6.12894758e-01 -2.20816518e+00 -1.05639652e-03\n",
|
||
|
|
" 2.00183667e-05 5.91435833e-04 -1.08031099e+00 8.96710234e-03\n",
|
||
|
|
" 4.86786144e-05 -2.75426884e-03 -5.23946253e-05 3.06615778e-07\n",
|
||
|
|
" 7.22203296e-07 3.07131064e-07 1.02830028e-06 -5.00502751e-07\n",
|
||
|
|
" 1.14320320e-02 -9.19801964e-01 1.26268262e-04 2.32567799e-06\n",
|
||
|
|
" 3.17366059e-06 4.04507073e+00 -1.47748622e-03 -2.59277724e-05\n",
|
||
|
|
" -1.10312486e-05 9.63613516e-06 1.17647833e-07 1.74207575e-07\n",
|
||
|
|
" -2.33445282e-08 -3.45841003e-08 -1.66442399e-06 -2.61561493e+00\n",
|
||
|
|
" 6.85840566e-03 2.45631773e-05 -3.37029852e-05 -9.50305201e-05\n",
|
||
|
|
" -5.05909782e-07 5.99063352e-08 -7.11912397e-08 -3.32265493e-10\n",
|
||
|
|
" 7.80788118e-06 3.42736622e-07 -3.37940292e-09 -4.19296109e-09\n",
|
||
|
|
" 5.19863930e-10 -1.59982882e-09 -1.38488071e-09 2.34894881e-11\n",
|
||
|
|
" -1.37425749e-09 -1.11708820e-09 -4.76537004e-11]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pol_reg3=LinearRegression().fit(X_train3,y_train)\n",
|
||
|
|
"print(\"R squared on test set:\", pol_reg3.score(X_test3,y_test))\n",
|
||
|
|
"print(\"Number of parameters:\", len(pol_reg3.coef_)+1)\n",
|
||
|
|
"print(\"Intercept:\", pol_reg3.intercept_)\n",
|
||
|
|
"print(\"Coefficients:\", pol_reg3.coef_)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"id": "dc28385e-5d15-4286-aa18-50ce4a37b29d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"y_pred3=pol_reg3.predict(X_test3)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"id": "52c51b93-7d1b-48d2-a530-2e7b4131706f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"MSE for test data: 0.009763178315716749\n",
|
||
|
|
"MAE for test data: 0.07445845502584257\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"MSE=0\n",
|
||
|
|
"MAE=0\n",
|
||
|
|
"for i in range(len(y_test)):\n",
|
||
|
|
" MSE += (y_test[i]-y_pred3[i])**2\n",
|
||
|
|
" MAE += abs(y_test[i]-y_pred3[i])\n",
|
||
|
|
"MAE=MAE/len(y_test)\n",
|
||
|
|
"MSE=MSE/len(y_test)\n",
|
||
|
|
"print(\"MSE for test data:\", MSE)\n",
|
||
|
|
"print(\"MAE for test data:\", MAE)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"id": "40e6b8a7-4b46-4e10-99b9-2187b84fcb7a",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(y_test[:100],'bo',markersize=3)\n",
|
||
|
|
"plt.plot(y_pred3[:100], 'r*',markersize=3)\n",
|
||
|
|
"plt.legend(['true','predicted'],loc='upper right')\n",
|
||
|
|
"plt.title('Polynomial Regression, d=3')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.19"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|