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notebook_wilshire.ipynb 63KB

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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "## Wilshire 5000 Predictions"
  8. ]
  9. },
  10. {
  11. "cell_type": "code",
  12. "execution_count": 1,
  13. "metadata": {},
  14. "outputs": [
  15. {
  16. "name": "stderr",
  17. "output_type": "stream",
  18. "text": [
  19. "C:\\Users\\DORIA\\anaconda3\\envs\\Global\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
  20. "C:\\Users\\DORIA\\anaconda3\\envs\\Global\\lib\\site-packages\\numpy\\.libs\\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll\n",
  21. "C:\\Users\\DORIA\\anaconda3\\envs\\Global\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n",
  22. " warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
  23. ]
  24. }
  25. ],
  26. "source": [
  27. "### Imports\n",
  28. "import wilshire\n",
  29. "import nn\n",
  30. "import tensorflow as tf\n",
  31. "import warnings\n",
  32. "warnings.filterwarnings('ignore')"
  33. ]
  34. },
  35. {
  36. "cell_type": "code",
  37. "execution_count": 2,
  38. "metadata": {},
  39. "outputs": [
  40. {
  41. "name": "stdout",
  42. "output_type": "stream",
  43. "text": [
  44. "[[0.016058204775629192, 0.0]]\n"
  45. ]
  46. }
  47. ],
  48. "source": [
  49. "### ARIMA Predictions\n",
  50. "\n",
  51. "x_train,x_test,y_train,y_test,maximum,index = nn.prepare_data()\n",
  52. "mse = nn.arima_pred(y_train,y_test,orders=[[2,1,1]],n=1)\n",
  53. "print(mse)"
  54. ]
  55. },
  56. {
  57. "cell_type": "code",
  58. "execution_count": 3,
  59. "metadata": {},
  60. "outputs": [
  61. {
  62. "name": "stdout",
  63. "output_type": "stream",
  64. "text": [
  65. "Model: \"sequential\"\n",
  66. "_________________________________________________________________\n",
  67. "Layer (type) Output Shape Param # \n",
  68. "=================================================================\n",
  69. "dense (Dense) (None, 1) 2 \n",
  70. "_________________________________________________________________\n",
  71. "dense_1 (Dense) (None, 64) 128 \n",
  72. "_________________________________________________________________\n",
  73. "dense_2 (Dense) (None, 64) 4160 \n",
  74. "_________________________________________________________________\n",
  75. "dense_3 (Dense) (None, 1) 65 \n",
  76. "=================================================================\n",
  77. "Total params: 4,355\n",
  78. "Trainable params: 4,355\n",
  79. "Non-trainable params: 0\n",
  80. "_________________________________________________________________\n",
  81. "Epoch 1/5\n",
  82. "6314/6314 [==============================] - 27s 4ms/step - loss: 0.0015\n",
  83. "Epoch 2/5\n",
  84. "6314/6314 [==============================] - 24s 4ms/step - loss: 0.0010\n",
  85. "Epoch 3/5\n",
  86. "6314/6314 [==============================] - 30s 5ms/step - loss: 9.9259e-04\n",
  87. "Epoch 4/5\n",
  88. "6314/6314 [==============================] - 25s 4ms/step - loss: 9.3238e-04\n",
  89. "Epoch 5/5\n",
  90. "6314/6314 [==============================] - 26s 4ms/step - loss: 9.0603e-04\n",
  91. "3/3 [==============================] - 0s 4ms/step - loss: 0.0094\n",
  92. "198/198 [==============================] - 1s 3ms/step - loss: 6.8375e-04\n",
  93. "----- ARIMA Test MSE -----\n",
  94. "ARIMA[2,1,1] : [0.016058204775629192, 0.0]\n",
  95. "----- DNN Test MSE -----\n",
  96. "DNN Snake : [0.009417124092578888, 0.0]\n"
  97. ]
  98. },
  99. {
  100. "data": {
  101. 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",
  102. "text/plain": [
  103. "<Figure size 432x288 with 1 Axes>"
  104. ]
  105. },
  106. "metadata": {
  107. "needs_background": "light"
  108. },
  109. "output_type": "display_data"
  110. }
  111. ],
  112. "source": [
  113. "## Simple training with different activations functions and plotting + printing results\n",
  114. "#activations = [tf.keras.activations.relu,nn.swish,nn.sinus_cosinus,nn.sinus,nn.snake]\n",
  115. "activations = [nn.snake]\n",
  116. "models,errors_train,errors_test = nn.training_testing(n=1,activations=activations,epochs= 5)\n",
  117. "nn.final_plot(models,errors_test,mse,activations=[\"Snake\"],orders_ARIMA = [\"[2,1,1]\"])"
  118. ]
  119. },
  120. {
  121. "cell_type": "code",
  122. "execution_count": 4,
  123. "metadata": {},
  124. "outputs": [
  125. {
  126. "data": {
  127. "image/png": 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",
  128. "text/plain": [
  129. "<Figure size 432x288 with 1 Axes>"
  130. ]
  131. },
  132. "metadata": {
  133. "needs_background": "light"
  134. },
  135. "output_type": "display_data"
  136. }
  137. ],
  138. "source": [
  139. "## Plot all a with already saved models (the name of the folders should not be changed)\n",
  140. "nn.plot_all_a(a=[\"1\",\"10\",\"20\",\"30\",\"50\",\"100\"])"
  141. ]
  142. },
  143. {
  144. "cell_type": "code",
  145. "execution_count": null,
  146. "metadata": {},
  147. "outputs": [],
  148. "source": []
  149. }
  150. ],
  151. "metadata": {
  152. "interpreter": {
  153. "hash": "dc4329e773707249108c96eb096f6a545cc45d5b5791118939d1012dd33cf165"
  154. },
  155. "kernelspec": {
  156. "display_name": "Python 3.9.7 64-bit ('Global': conda)",
  157. "language": "python",
  158. "name": "python3"
  159. },
  160. "language_info": {
  161. "codemirror_mode": {
  162. "name": "ipython",
  163. "version": 3
  164. },
  165. "file_extension": ".py",
  166. "mimetype": "text/x-python",
  167. "name": "python",
  168. "nbconvert_exporter": "python",
  169. "pygments_lexer": "ipython3",
  170. "version": "3.9.7"
  171. },
  172. "orig_nbformat": 4
  173. },
  174. "nbformat": 4,
  175. "nbformat_minor": 2
  176. }