From b3f71b1d6c931aacd800d1145b59f0218fdf61af Mon Sep 17 00:00:00 2001 From: morisset Date: Thu, 26 Sep 2019 20:40:22 -0700 Subject: [PATCH 1/4] Adding save facility for Keras-TF Models --- docs/ComparePolynom.ipynb | 82 +++- docs/SaveRestore.ipynb | 686 ++++++++++++++++++++++++++++ mwinai/Regressor/RegressionModel.py | 141 +++--- mwinai/version.py | 2 +- 4 files changed, 835 insertions(+), 76 deletions(-) create mode 100644 docs/SaveRestore.ipynb diff --git a/docs/ComparePolynom.ipynb b/docs/ComparePolynom.ipynb index 6576b4c..47900f4 100644 --- a/docs/ComparePolynom.ipynb +++ b/docs/ComparePolynom.ipynb @@ -50,9 +50,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/morisset/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", - " warnings.warn(msg, category=DeprecationWarning)\n", - "Using TensorFlow backend.\n" + "/Users/christophemorisset/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", + " warnings.warn(msg, category=DeprecationWarning)\n" ] } ], @@ -97,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -152,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -227,23 +226,46 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instantiation. V 0.15\n", + "Training set size = 30, Test set size = 0\n", + "Train data scaled.\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 0\n", + "Training set size = 30, Test set size = 0\n", + "Regression Model SK_ANN\n", + "Training 1 inputs for 1 outputs with 30 data\n", + "RM trained, with 4000 iterations. Score = 0.969\n", + "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", + " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=4, learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=4000, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=10, shuffle=True, solver='adam', tol=1e-06,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)\n", + "Training time 1.4 s.\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/morisset/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n", + "/Users/christophemorisset/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n", " % self.max_iter, ConvergenceWarning)\n" ] } ], "source": [ "RM = mwinai.manage_RM(RM_type='SK_ANN', X_train=X_train, y_train=y_train_true, scaling=True,\n", - " verbose=False, random_seed=10)\n", + " verbose=True, random_seed=10)\n", "RM.init_RM(hidden_layer_sizes=(4), \n", - " tol=1e-6, max_iter=1000, \n", + " tol=1e-6, max_iter=4000, \n", " activation='tanh',\n", " solver='adam')\n", "RM.train_RM()" @@ -258,9 +280,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data scaled.\n", + "Training set size = 30, Test set size = 30\n", + "Predicting from 1 inputs to 1 outputs using 30 data in 0.00 secs.\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Predicting from 1 inputs to 1 outputs using 100 data in 0.00 secs.\n" + ] + } + ], "source": [ "RM.set_test(X_train, scaleit=True)\n", "RM.predict(scoring=False)\n", @@ -281,12 +316,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -320,12 +355,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -354,7 +389,7 @@ " rms_train = np.sqrt(mean_squared_error(y_train,y_train_true))\n", " rms_test = np.sqrt(mean_squared_error(y_test,y_test_true))\n", " ax.plot(X_test, y_test, label=\"Model\")\n", - " ax.plot(X_test, y_test_true, label=\"True function\")\n", + " #ax.plot(X_test, y_test_true, label=\"True function\")\n", " ax.scatter(X, y, edgecolor='b', s=20, label=\"Samples\")\n", " ax.set_xlabel(\"x\")\n", " ax.set_ylabel(\"y\")\n", @@ -385,7 +420,7 @@ " rms_test = np.sqrt(mean_squared_error(y_test, y_test_true))\n", " ax = axes[1,i]\n", " ax.plot(X_test, y_test, label=\"Model\")\n", - " ax.plot(X_test, y_test_true, label=\"True function\")\n", + " #ax.plot(X_test, y_test_true, label=\"True function\")\n", " ax.scatter(X, y, edgecolor='b', s=20, label=\"Samples\")\n", " ax.set_xlabel(\"x\")\n", " ax.set_ylabel(\"y\")\n", @@ -396,6 +431,13 @@ " rms_train, rms_test))\n", "f.tight_layout()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/SaveRestore.ipynb b/docs/SaveRestore.ipynb new file mode 100644 index 0000000..bd97821 --- /dev/null +++ b/docs/SaveRestore.ipynb @@ -0,0 +1,686 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christophemorisset/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", + " warnings.warn(msg, category=DeprecationWarning)\n" + ] + } + ], + "source": [ + "try:\n", + " import mwinai\n", + "except:\n", + " !pip install -U git+https://github.com/taller-mexicano-de-nebulosas-ionizadas/AI.git\n", + " import mwinai" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def true_fun(x):\n", + " return np.cos(1.5 * np.pi * x)\n", + "def true_fun2(x):\n", + " return np.sin(1.5 * np.pi * x)\n", + "\n", + "# A random seed to reproduce the results\n", + "np.random.seed(0)\n", + "\n", + "# The number of points used to fit the function\n", + "n_samples = 30\n", + "\n", + "# Noise to be added to the points used to fit the function\n", + "noise = 0.1\n", + "\n", + "# The training set: n_samples X points, with the noisy correspoing y \n", + "X_train = np.sort(np.random.rand(n_samples))\n", + "y_train = true_fun(X_train) + np.random.randn(n_samples) * noise\n", + "y_train2 = true_fun2(X_train) + np.random.randn(n_samples) * noise\n", + "\n", + "# The set of points to verify the fit quality\n", + "X_test = np.linspace(0, 1, 100)\n", + "y_test = true_fun(X_test)\n", + "y_test2 = true_fun2(X_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instantiation. V 0.15\n", + "Training set size = 30, Test set size = 100\n", + "Train data scaled.\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Training set size = 30, Test set size = 100\n", + "Regression Model SK_ANN\n", + "Training 1 inputs for 1 outputs with 30 data\n", + "RM trained, with 100 iterations. Score = 0.950\n", + "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", + " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(100, 100), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=100, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=10, shuffle=True, solver='adam', tol=1e-06,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)\n", + "Training time 0.1 s.\n", + "Score = 0.975\n", + "Predicting from 1 inputs to 1 outputs using 100 data in 0.00 secs.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christophemorisset/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + } + ], + "source": [ + "RM = mwinai.manage_RM(RM_type='SK_ANN', X_train=X_train, y_train=y_train, scaling=True,\n", + " X_test = X_test, y_test=y_test, verbose=True, random_seed=10)\n", + "RM.init_RM(hidden_layer_sizes=(100,100), \n", + " tol=1e-6, max_iter=100, \n", + " activation='tanh',\n", + " solver='adam')\n", + "RM.train_RM()\n", + "RM.predict(scoring=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM.X_test_unscaled, RM.pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RM save to RM1\n", + "RM loaded from RM1.mwinai_sk\n", + "Training set size = 30, Test set size = 100\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Predicting from 1 inputs to 1 outputs using 100 data in 0.00 secs.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uhxlz97cUvhTsJV4RyjWLm6esnPaI2iXLVHWWVp46qsnVNXFpKIdvZvuAt7v7s2Z2NvAdd5/ym2RmL7j7mbU8t3L4UpdK3TWBMc7g8bf+DRes/CCQvk6HkrA6UjcnPZj/j/ps7pn5XjZ+4tPNG18dKuXwG53hv9bdnwUo/PmaMse9zMyGzOwxM1tdYaBrC8cNHTx4sMGhSS5V2Tylkxe54MnPAeH7kW7Yvof+4dGEBistNWVWX5k7jJyczV+M38iCF7/CRcfv4L4XljV5kPGqmsM3s28Brwt56OM1vM457n7AzF4P7DCzPe7+5OSD3H0rsBWCGX4Nzy8SiLR5StBs7Ue/fg9j4xN/YYtXWWqWn2F1zOrHOIO/Gr9+SrVXu60FVQ347v7Oco+Z2S/M7OySlM5zZZ7jQOHPp8zsO8BSYErAF4nFkjXBV6X0ztFnuMU/z/PTjk/5JVYFT4bVVGZ5urTy8Td8hG/uPBdOVqjXbwONpnQGgGsL318LfH3yAWZ2lpmdUfh+NrAceKLB1xWprkp6p1i9M1m7zdokglrbIHTNgyu2wsaj8NHHuWDlB9l0xfmp3Ke2Fo3W4W8GtpnZ9cDTwFUAZtYH3ODu7wfeBNxlZicJ3mA2u7sCvjRfhPTOXPvlhNvtOGuTMuops6xQWlnXzlYpoyttJR/KpHcc+AVz2HT8KoZeeYmqdLKinitkU3zBVC10pa1ImYuzjOBq3Ntf/s9w2VJYonYLba2eK2Tb4IKpuKg9suRDtYuzxseC/K5aK7evGsssgba5YCoumuFLfhSrdyo0W9NWia1T90VwmtVHphm+5E+17eOauIm0hKv7IriaZvWFDUdyNqsvpYAv+VOlXBM4dXGW0jvJqHmrwQbLLPMY7EEpHcmjKFfjgtI7Cappq8FaKnBymropRzN8yafi3rhX3F15tq/0TiLKXew24f56ZvUK9hMo4Eu+RWitrPRO861bsYjOjukT7ptwEVwtufqOzuCNvCR10z88yvLNO1iw/hss37wjtw3ylNIRidh7R+md5ilW40yp0pn+KNxWQwVOyMVTxQVh7WusK21FTouSG+6aF8wcpfliytUv37yD0ZC1gKzua9zMfvgi2aH0TjrEnKuvaUE44xTwRUoVF3OrBf0Hb1bQb4YGc/VhIi0I54QCvkiYarX6asUQrzpm9TvP/zTLH5pddSG26oJwjmjRViRMQrX62lOXunL1/SeWFxZig79TaSG27IJw3v6d0aKtSHVVNkY/pcb2upOrRyCYebbjxhp1qbUHTsm/b94WYmuh9sgijSjTWnmKGmf7ldoJZDbgx7QpiRZi66Mcvkg1Uap3imrI7ecuaE1ZkI0Q7MtU4Gghtj4K+CJRRG3FUBShkic3QavWBVmoWoGjhdj6KOCL1CLG2X4uglaTNiVZvbQnE5uKJ02LtiL1qqG65KXpL+Nv7Qbue2HZhCqRWqp02qqiR5uStIwWbUWaIWrpJjDjxG/4lH+W98+czWd+tYYN248DwUw1StBui34w9SzIFo/LyAbiaaeAL9KIQuO1nQN3sfiHf00nL5Y91Ax67RCbO+6BcdgyODNysE59Rc+UTzsRF2RDgnxbfZJpMwr4Ig3qHx5lw85zueTE9dwyYxs9dgiz8sfPsuPc3vF5Ro9tg92bIs1qU1vRE3Pqpi0+ybQxLdqKNKg4+x44eREXHb+DPx+/kWM+s+LfMYPeaYdg+1rY2FW1jDNVFT3FqpuNXcH4Y1yQrXmrQ6mJZvgiDZo8yx44eRGME8z2px2iwmSfU6mPo88EwXP7B0JTHetWLAq9Kjexip6y+fmIRR8RF2RT+0kmIxTwRRo0t7tzymX+AycvYtesS+j71TfZ1HEPs+x4hGcqCf6TrthtST+YRoN8HQuyYf+WxfulcQr4Ig2qNPveMgjrf0Wk3P4ExRr+h2+Fhe+C//0vVh8dYXVXL/zJJ2HJHzTnZBoO8gV1Vt20/JNMxqkOXyQG5SpLShchV057hM2RZ/uVxFzKWFc5ZRkx1NKrSqcxlerwFfBFmqw0gF175g+4peMBZo09S8PBFU4/R+ergptjh6HzrBq+fz6GcaiWPk0U8EXSKM6ZdeIU5NNKV9qKpFHhoi2gvnr2xCnItzsFfJE0KAb/WnZ/SoSCfJY0FPDN7CpgI/AmYJm7h+ZgzOxS4HZgOnCPu29u5HVF0qrhBccJ/XlGoKv3VJVOcqkfBfmsanSG/zhwBXBXuQPMbDpwJ3AJMALsNLMBd3+iwdcWSZXY2gKUpnoma1reP/kgr2qc5DUU8N39JwBWubh4GbDf3Z8qHHs/sApQwJdMSaTBWWjefyS0AsfHDnOUMznpTrf9mvGOV3LGjOlTK3a6ehOfyatnTmskkcPvAUpXokaA3w470MzWAmsBzjnnnOaPTCRGibcFqPBJ4BP9e/jXx56eMP/vPJGeDdLLvTluHNibivFlVdXmaWb2LTN7PORrVcTXCJv+h34Odfet7t7n7n1z5syJ+PQi6ZCWBmf9w6NTgj2kqwlZuTfBI2Pj9A+PJjya/Kga8N39ne6+OOTr6xFfYwQo3Q+uFzhQz2BF0iwtWxZuGdxXNrOfliZkld4E0/KmlEVJtEfeCSw0swVmNhO4GhhI4HVFEpWWfVYrBfW0NCGr9CaYljelLGq0LPOPgM8Bc4BvmNmP3H2Fmc0lKL+8zN1fMrObgEGCssx73X1vwyMXSaGoWxY2U7mOk0blQBtFXJU1q5f28OkH93L42PiUx9LyppRFDc3w3f3f3b3X3c9w99e6+4rC/Qfc/bKS4x5y9ze6+xvc/e8aHbSIlBeWWjLgTy88p6E3o2JlzeiRMZzTlTX15tw/dfmbU5ECyxNdaSuSMc3qnR932WlLevznnAK+SAY1I7XUjLLTNKTA8kR72opIJGkpO5X6KeCLSCRpKTuV+imlIyKRKOfe/hTwRSQy5dzbm1I6IiI5oYAvIpITCvgiIjmhgC8ikhMK+CIiOaGALyKSE+be7A2R62NmB4GfxfR0s4FDMT1X2uXpXEHnm2V5OleI73zPdffQHaRSG/DjZGZD7t7X6nEkIU/nCjrfLMvTuUIy56uUjohITijgi4jkRF4C/tZWDyBBeTpX0PlmWZ7OFRI431zk8EVEJD8zfBGR3FPAFxHJicwEfDO71Mz2mdl+M1sf8vgZZvZA4fHvm9n85EcZnwjn+zEze8LMdpvZw2Z2bivGGZdq51ty3JVm5mbWtuV8Uc7VzNYU/n/3mtlXkh5jnCL8LJ9jZt82s+HCz/NlrRhnHMzsXjN7zsweL/O4mdkdhX+L3Wb21lgH4O5t/wVMB54EXg/MBH4MnDfpmBuBLxS+vxp4oNXjbvL5/j4wq/D9h7J+voXjXgF8F3gM6Gv1uJv4f7sQGAbOKtx+TavH3eTz3Qp8qPD9ecBPWz3uBs73d4G3Ao+Xefwy4D8BAy4Evh/n62dlhr8M2O/uT7n7ceB+YNWkY1YB9xW+/xpwsZlZgmOMU9Xzdfdvu/uxws3HgN6ExxinKP+/AH8DfAb4TZKDi1mUc/0AcKe7HwZw9+cSHmOcopyvA68sfN8FHEhwfLFy9+8Cz1c4ZBXwZQ88BnSb2dlxvX5WAn4P8EzJ7ZHCfaHHuPtLwFHg1YmMLn5RzrfU9QSzhnZV9XzNbCkwz93/I8mBNUGU/9s3Am80s0fN7DEzuzSx0cUvyvluBN5rZiPAQ8BHkhlaS9T6u12TrGxxGDZTn1xvGuWYdhH5XMzsvUAf8HtNHVFzVTxfM5sG3AZcl9SAmijK/+0MgrTO2wk+uf23mS129yNNHlszRDnfa4Avufs/mtnvAP9SON+TzR9e4poap7Iywx8B5pXc7mXqx75Tx5jZDIKPhpU+WqVZlPPFzN4JfBxY6e4vJjS2Zqh2vq8AFgPfMbOfEuQ+B9p04Tbqz/LX3X3c3f8P2EfwBtCOopzv9cA2AHf/HvAygkZjWRTpd7teWQn4O4GFZrbAzGYSLMoOTDpmALi28P2VwA4vrJK0oarnW0hx3EUQ7Ns5xwtVztfdj7r7bHef7+7zCdYsVrr7UGuG25AoP8v9BIvymNlsghTPU4mOMj5Rzvdp4GIAM3sTQcA/mOgokzMAvK9QrXMhcNTdn43ryTOR0nH3l8zsJmCQYNX/Xnffa2a3AkPuPgB8keCj4H6Cmf3VrRtxYyKe7xbgTODfCmvTT7v7ypYNugERzzcTIp7rIPAuM3sCOAGsc/dftm7U9Yt4vn8J3G1mHyVIb1zXrpM1M/sqQSpudmFN4lNAB4C7f4FgjeIyYD9wDPizWF+/Tf/dRESkRllJ6YiISBUK+CIiOaGALyKSEwr4IiI5oYAvIpITCvgiIjmhgC8ikhP/DybS2kTMuxT5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "RM.save_RM('RM1')\n", + "RM_back = mwinai.manage_RM(RM_filename='RM1')\n", + "RM_back.set_test(X_test, scaleit=True)\n", + "RM_back.predict(scoring=False)\n", + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM_back.X_test_unscaled, RM_back.pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instantiation. V 0.15\n", + "Training set size = 30, Test set size = 100\n", + "Train data scaled.\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Training set size = 30, Test set size = 100\n", + "Regression Model SK_ANN\n", + "Training 1 inputs for 2 outputs with 30 data\n", + "RM trained, with 100 iterations. Score = 0.937\n", + "MLPRegressor(activation='tanh', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", + " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(100, 100), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=100, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=10, shuffle=True, solver='adam', tol=1e-06,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)\n", + "Training time 0.1 s.\n", + "Score = 0.937\n", + "Predicting from 1 inputs to 2 outputs using 100 data in 0.00 secs.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/christophemorisset/anaconda3/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/christophemorisset/anaconda3/lib/python3.7/site-packages/sklearn/base.py:420: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').\n", + " \"multioutput='uniform_average').\", FutureWarning)\n", + "/Users/christophemorisset/anaconda3/lib/python3.7/site-packages/sklearn/base.py:420: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').\n", + " \"multioutput='uniform_average').\", FutureWarning)\n" + ] + } + ], + "source": [ + "RM = mwinai.manage_RM(RM_type='SK_ANN', X_train=X_train, y_train=np.array([y_train, y_train2]).T, scaling=True,\n", + " X_test = X_test, y_test=np.array([y_test, y_test2]).T, verbose=True, random_seed=10)\n", + "RM.init_RM(hidden_layer_sizes=(100,100), \n", + " tol=1e-6, max_iter=100, \n", + " activation='tanh',\n", + " solver='adam')\n", + "RM.train_RM()\n", + "RM.predict(scoring=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 2)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RM.pred.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM.X_test_unscaled, RM.pred[:,0])\n", + "plt.scatter(X_train, y_train2)\n", + "plt.scatter(RM.X_test_unscaled, RM.pred[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RM save to RM2\n", + "RM loaded from RM2.mwinai_sk\n", + "Training set size = 30, Test set size = 100\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Predicting from 1 inputs to 2 outputs using 100 data in 0.00 secs.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "RM.save_RM('RM2')\n", + "RM_back2 = mwinai.manage_RM(RM_filename='RM2')\n", + "RM_back2.set_test(X_test, scaleit=True)\n", + "RM_back2.predict(scoring=False)\n", + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM_back2.X_test_unscaled, RM_back2.pred[:,0])\n", + "plt.scatter(X_train, y_train2)\n", + "plt.scatter(RM_back2.X_test_unscaled, RM_back2.pred[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instantiation. V 0.15\n", + "Training set size = 30, Test set size = 100\n", + "Train data scaled.\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Training set size = 30, Test set size = 100\n", + "WARNING:tensorflow:From /Users/christophemorisset/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", + "WARNING:tensorflow:From /Users/christophemorisset/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense (Dense) (None, 100) 200 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 100) 10100 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 1) 101 \n", + "=================================================================\n", + "Total params: 10,401\n", + "Trainable params: 10,401\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Regression Model K_ANN\n", + "Training 1 inputs for 1 outputs with 30 data\n", + "WARNING:tensorflow:From /Users/christophemorisset/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "RM trained. Score = nan\n", + "\n", + "Training time 1.7 s.\n", + "Predicting from 1 inputs to 1 outputs using 100 data in 0.07 secs.\n" + ] + } + ], + "source": [ + "RM = mwinai.manage_RM(RM_type='K_ANN', X_train=X_train, y_train=y_train, scaling=True,\n", + " X_test = X_test, y_test=y_test, verbose=True, random_seed=10)\n", + "RM.init_RM(hidden_layer_sizes=(100,100), \n", + " tol=1e-6, epochs=100, \n", + " activation='tanh',\n", + " solver='adam')\n", + "RM.train_RM()\n", + "RM.predict(scoring=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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eZnC2T2IzpgqAwAfyIpJZPvOy81NAJ+1Vk3UM2gJ50dKCrlqys9CraPeWTQuBD3SiqGb5nJVu+Odlr5qso6QD5EEks3yqSab+H3cNv0gDwtTwgSKJLfzLRV//jyuUizYgTOADRVU5xVOSZo+pE38KaFXRBoTrBT43MQfybO3W6qEb6U8Bwe+dPXbuqfKbuKf8QcCA8DkEPlBE5R8EsZWAsvFBcFlPd9UefhEHhCnpADgnkfp/Lc2VhsLW/Knhl71G4AOoKpH6fxgLPwhOdL9Jd/7fB/XQyXedPapeiDNLJ3iNwAfS05FBlOpPAeec8dLZj/nrZCb16DW9Yr1605a/yMRgcVoIfCCDclFqyMxPAeWqlIbOti1bM4jiQOADGZTr6YKZ/CAol+2ppO1gWiaQQbmeLthwOmjaHwTZmEGUNAIfSEkhpwvm6YOgA8tElHSABFQbnJXUVA2/Iwd425WZD4IwslEmooYPpKje4Kwk5pK3gg+C2mcj8IH0RDE4m+sB3ihV/SA4ntEPhXg+CBi0BWJWr9wSxeBsrgd4K7RVuqo1RlAuMz8d1BgveOSO0vcx9P4JfKBNleWWyelZ7dz7rCRp87o+9VzUpeMn5hb8vmYGZ4sywNvozzISWR84npsttYPAB7Jn98j4ebV1SZqdO63dI+OSpNd+fmrB7+labGcHbsPYvmF11Rp+M+/RCer9WcY+VtHUB0HMZaKZiejeqwyBD7SpXrll98i45s4sDIJfvmBJUwE2f2zeZ+lksnSVRplo6fLmf08IBD7QpnrlllpBNTO7sMTTyOZ1fbkL+EodW7qKskzU1V0auI0BNzEH2rR9w2p1dy0+77n5cgs3525OvT/LjrR2q/S5g9KuaelPXyx97ZqRtuwJbkBvpVk63W8sfb/08tLN6WOat08PH2hTo3JLEWrvUSlK6SpUmSgGzMMHYlbIFbJIDfPwgRQVofaOzkANHwAKgsAHgIKgpANEiHo9sqytHr6ZfdjMDpnZGTOrOkgQHHe9mY2b2WEz29HOOYGsmt8WYHJ6Vq5z2wIMjU2m3bRMGhqb1PqB/Vq5Y5/WD+znzykB7ZZ0DkraIumxWgeY2WJJ90u6QdIaSTeb2Zo2zwtkTqMtFnAOH47paKuk4+7PS5KZ1TvsGkmH3f2F4NgHJG2S9Fw75wayJkvbAgyNTWrX8CFNByt6L76oS3d94K2ZKS/V+nDcNXwoM23MoyQGbfskHSl7PBE8t4CZbTOzUTMbnZqaSqBpQHSysqp2aGxS27/7zNmwl6TjJ+a0/aFnMtODrvUhOD07l5k25lHDwDez75vZwSpfm0Keo1r3v+pqL3ff4+797t7f29sb8u2BbMjKtgC1NmybO+2ZKS/V+xDMShvzqGFJx93f1+Y5JiRdXvZ4uaSjbb4nkDlZ2RagXgkpKzdM2b5htT774NNVX8tKG/MoiWmZT0paZWYrJU1KuknSRxI4L5C4LKyqrbXj5PxrWbB5XZ++8Mihtm8Mg+a0Oy3zD8xsQtI7Je0zs5Hg+cvM7HuS5O6nJN0uaUTS85IG3f1Qe80GUMv2DavVtWhhJbXZm65UE+VUyrs+8NZMlMCKhM3TgByKY5ZO5e0HpVJA37Plqpbfl4Vq0au3eRqBDyCU9QP7q5aK+nq69aMd702hRaimXuCzlw6AULK0zgCtIfABhJKVdQZoHYEPIJSsrDNA69gtE0AoWVlngNYR+ABCy8I6A7SOkg4AFASBDwAFQeADQEEQ+ABQEAQ+ABQEgQ8ABZHZvXTMbErSSxG93TJJr0b0XllXpGuVuN48K9K1StFd75vdveodpDIb+FEys9FamwnlTZGuVeJ686xI1yolc72UdACgIAh8ACiIogT+nrQbkKAiXavE9eZZka5VSuB6C1HDBwAUp4cPAIVH4ANAQeQm8M3sejMbN7PDZrajyusXmtmDwes/NrMVybcyOiGu9/Nm9pyZHTCzR83szWm0MyqNrrfsuA+ZmZtZx07nC3OtZrY1+Ps9ZGb/kHQboxTi3/IVZvYDMxsL/j3fmEY7o2Bm3zCzV8zsYI3Xzcy+FPxZHDCzt0faAHfv+C9JiyX9t6RflXSBpGckrak45tOSvhx8f5OkB9Nud8zX+x5JFwXffyrv1xsc93pJj0l6QlJ/2u2O8e92laQxSRcHj38l7XbHfL17JH0q+H6NpJ+k3e42rve3Jb1d0sEar98o6V8kmaR3SPpxlOfPSw//GkmH3f0Fdz8p6QFJmyqO2STpW8H3D0m6zswswTZGqeH1uvsP3P1E8PAJScsTbmOUwvz9StKfS/orST9PsnERC3Ott0m6392PS5K7v5JwG6MU5npd0huC75dKOppg+yLl7o9JOlbnkE2Svu0lT0jqMbNLozp/XgK/T9KRsscTwXNVj3H3U5JmJF2SSOuiF+Z6y31CpV5Dp2p4vWa2TtLl7v7PSTYsBmH+bt8i6S1m9iMze8LMrk+sddELc727JH3UzCYkfU/SZ5JpWiqa/b/dlLzc4rBaT71yvmmYYzpF6Gsxs49K6pf0O7G2KF51r9fMFkm6V9KtSTUoRmH+bpeoVNZ5t0o/uf2bmV3p7tMxty0OYa73ZknfdPe/NrN3Svq74HrPxN+8xMWaU3np4U9Iurzs8XIt/LHv7DFmtkSlHw3r/WiVZWGuV2b2Pkl/Jmmju/8iobbFodH1vl7SlZJ+aGY/Uan2OdyhA7dh/y0/7O5z7v6ipHGVPgA6UZjr/YSkQUly93+X9EsqbTSWR6H+b7cqL4H/pKRVZrbSzC5QaVB2uOKYYUm3BN9/SNJ+D0ZJOlDD6w1KHF9RKew7ucYrNbhed59x92XuvsLdV6g0ZrHR3UfTaW5bwvxbHlJpUF5mtkylEs8LibYyOmGu92VJ10mSmf2mSoE/lWgrkzMs6WPBbJ13SJpx959G9ea5KOm4+ykzu13SiEqj/t9w90NmdrekUXcflvR1lX4UPKxSz/6m9FrcnpDXu1vS6yR9NxibftndN6bW6DaEvN5cCHmtI5J+z8yek3Ra0nZ3/1l6rW5dyOv9Y0lfNbPPqVTeuLVTO2tm9h2VSnHLgjGJuyR1SZK7f1mlMYobJR2WdELSxyM9f4f+uQEAmpSXkg4AoAECHwAKgsAHgIIg8AGgIAh8ACgIAh8ACoLAB4CC+H8g3qMX9IGMUgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM.X_test_unscaled, RM.pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RM save to RM3\n", + "Instantiation. V 0.15\n", + "Training set size = 0, Test set size = 0\n", + "RM loaded from RM3.mwinai_k0\n", + "RM loaded from RM3.mwinai_k1\n", + "Training set size = 30, Test set size = 100\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Predicting from 1 inputs to 1 outputs using 100 data in 0.27 secs.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "RM.save_RM('RM3')\n", + "RM_back3 = mwinai.manage_RM(RM_filename='RM3', verbose=True)\n", + "RM_back3.set_test(X_test, scaleit=True)\n", + "RM_back3.predict(scoring=False)\n", + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM_back3.X_test_unscaled, RM_back3.pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instantiation. V 0.15\n", + "Training set size = 30, Test set size = 100\n", + "Train data scaled.\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Training set size = 30, Test set size = 100\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_3 (Dense) (None, 100) 200 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 100) 10100 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 2) 202 \n", + "=================================================================\n", + "Total params: 10,502\n", + "Trainable params: 10,502\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Regression Model K_ANN\n", + "Training 1 inputs for 2 outputs with 30 data\n", + "RM trained. Score = nan\n", + "\n", + "Training time 1.8 s.\n", + "Predicting from 1 inputs to 2 outputs using 100 data in 0.15 secs.\n" + ] + } + ], + "source": [ + "RM = mwinai.manage_RM(RM_type='K_ANN', X_train=X_train, y_train=np.array([y_train, y_train2]).T, scaling=True,\n", + " X_test = X_test, y_test=np.array([y_test, y_test2]).T, verbose=True, random_seed=10)\n", + "RM.init_RM(hidden_layer_sizes=(100,100), \n", + " tol=1e-6, epochs=100, \n", + " activation='tanh',\n", + " solver='adam')\n", + "RM.train_RM()\n", + "RM.predict(scoring=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM.X_test_unscaled, RM.pred[:,0])\n", + "plt.scatter(X_train, y_train2)\n", + "plt.scatter(RM.X_test_unscaled, RM.pred[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RM save to RM4\n", + "RM loaded from RM4.mwinai_k1\n", + "Training set size = 30, Test set size = 100\n", + "Test data scaled.\n", + "Training set size = 30, Test set size = 100\n", + "Predicting from 1 inputs to 2 outputs using 100 data in 0.53 secs.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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n7XyUao96fQNwTvX64osCflTVz/Z4wsVy/UrtFNym3ZtGVXeB9017fhZzXX8/hzZszOsYpbwo4EeV8vlFd/DEQd+Pj7oz17zz9mrHIOko4EeV8vlFl03vHTid3pn/wvNpWzIovSNeFPCjLGM+/1Wldwoom947Y2VK8Si9I6ko4GcpXVVFyfLM56P0TgHlctOen+ZrSu/IWLrxKguhb6MwUWq/UNIytWPQZirRohuv8iSbqoqSovYLJU3pHfFLAT8L2VRVlJxEPt8z6KtcM6yU3hG/FPCzkG1VRUlSuWZJSlTwqHpH0lHAz0IuVRUlQ+WaJU3pHUlHAT8LoW+FnC9qv1CyfKV3AtjvWcJJVTriTe2US1q66h1tl1i+VKUjE6N8fknr/P3f4VR16ndpyudHkwK+eFM+v2R17O9g1eR2vvbe2JaIqd7HK58fPQr4kp7y+SUpcc/IExdUcvPNVZ799FWuGS0K+OKP2imXlLH3hrw21ftYpXeiQwFf/FE+v6SMvTfkO5ca/VXexyu9Ew2RDPhl2QCt0JTPLylj7xl54oJK/ub3JjMwo8Hza1SuWf4iF/D9bCsnHtROuaRMrpw88nHD5AaWr/wSC37y7941+s4pn1/mIhfwy7YBWpDUTjnUEpOa3lO9I4/1D57+mU93N67y+eUtcgG/rBugBSVdPh+U3imyTJOaTHfjKp9fvvIS8M3sPWb2opm9bGarUzw/2cy+F3/+P8xsTj5edyIi0QCt0NROOdT8TGoSzda89sdVPr885RzwzawSuBd4L/BW4INm9tYxh30UOOac+01gA/AXub7uREWiAVoQ1E45tLKZ1FQ1NqY+ifL5ZSkfM/xLgJedc/udc6eAB4GrxhxzFfBA/OMtwOVmHlOLAotMA7SgqFwzdLKZ1CifHy1pKnN9mwUkd9g6APyW1zHOuUEz6wXOBo4kH2RmK4GVAOeee24ehpZa89xmBfh8STRPe3Rt6kZriXy+mqwFJvGzvWn3Jg6eOMjMupm0LmpN+TOfaJ52aMPGlI3WEvl8NVkrDzl3yzSz9wPLnXM3xj//A+AS59ynk47ZFz/mQPzzn8WPec3rvOqWWYLaGkjdtcWgrSfo0UiWXpj/VvCIB+quWToK3S3zAJCcyJ0NjJ0qjBxjZlVAPXA0D68tYaL2CyXNM5+P0jv5sHVPJ0vW7eS81R0sWbeTrXs6Ax9DPgL+08A8MzvPzCYB1wPbxhyzDfhw/ONrgZ0urI34ZeKUzy9p2i2rcLbu6WTNQ8/S2dOHAzp7+ljz0LOBB/2cA75zbhD4FLAdeAHY7JzbZ2ZrzWxF/LBvAmeb2cvAnwDjSjelDKj9QknTblmFs377i/QNDI16rG9giPXbXwx0HNrxSgrDM5+PdssqAdotK7/OW93htbrFz9flt4BEO15J8NR+oaSpXDO/mhpSpzq9Hi8UBXwpDLVfKGlqv5Bfq5afT2115ajHaqsrWbX8/EDHoYAvhaH2CyVP7Rfy5+qFs7jrmouY1VCLAbMaarnrmou4euGsQMehHL4U3oYLU9+UBcrnlwCvfL4DBmc08MZVtyufHyLK4UtxqVyzpHnl8w2oPtTDgT/7nPL5JUIBXwpP5ZolLZHPP1pfmbLSpOL1AeXzS4QCvgQj425ZyueHWX1LC5/4ZIVXoS2DXV3qrlkCFPAlWGq/ULJm1s3ktanez6tcM/wU8CVYyueXrNZFrWy5bDL9aXrsqlwz3PLRHlnEvwK1U966p5P121+kq6ePpoZaVi0/P/CSt3LXPLcZVsLmqi/z3h1HmXY8dYJO5ZrhpbJMKZ48tV9INKZK7lVSW11ZlDrnKFH7hXBSWaaEk8/2C5nayoalMVXUqP1C6VHAl+Lx0X7h5CN3ZGwr29XTl/LLO3v6itZ3PArUfsFbGHrfp6IcvhRPpnw+UNN30HP2nkjXNDXU0pkm6K956FkApXcKoL6lhfqWFs/dsqKQzx+7fvTut0znn57pHPm5DdPPoGb4UlyJ+nyPm7LMOR6f9BlWVDw+6vHkWX2qxlTJlN4pPM/dspwr6/r8VBub/MNTvwxtilEBX8LBI71jBrMrjrCu+v5RQT+5rWxyYyovXmkfyY+o5vNTrR95lcGE4WdQAV/CIUP7hSl2ituqYvX5qdrKXr1wFk+svswz6AfddzxqoprPzyaIh+FnUAFfwiND+4Umey1jW9mw9B2Poii2U/YK4mO/A2H5GVTAl/DxKNesMMcTkz/D1ZVPeH5pWPqOR1mU8vleE4zff/u5ofwZ1I1XEj57N8dq8Ac83i5X18bSP+qhH0q97e10f/4OXH9/yuetpobGO9eWzU1ZYbvLO92NVwr4Ek57N6ct16T+nHj6R8Kot72dQxs2pr0Td97ORwMeVTToTlspPRnbKb+q7pohljGfr3bKRaGAL+Hms/2ChJNnPp/yLtcMKwV8CTcf7Re0W1Z4pavPh1i55nNfXMOCBxawbMsyOvZ3BDi66FHAl3DLtD0iKL0TYpnq8wEaeodwOLpPdNP2ZFvgQT+sfW8KQYu2Ujo2XOi9iAuq3gk5r3bKDjgyFb5zqfHEBZU01jWy49odgYypHFtra9FWyoPSOyXNK71jwPTjcNPDjiX7hjh44mBgY4paa20FfCkdvtI72gw9rDKld2oG4YbHHDPrZgY2Jq/WCGHoe1MICvhSWjJ010y1GXqUcrRhl6lcc9rx2N65QfFqjRCGvjeFoIAvpcnnZuip2teO3UBFgpeuXPPNN34lsFLNqPVeUsCX0pQpvRPP50ctR1sqZtx6C8OTq8c9bgRbnx+13ks5VemY2VnA94A5wCvAdc65YymOGwKejX/6S+fcikznVpWO+JZmM/QDw9O4e/A6tg0vHfW4AT9f11z4sUlKHfs72H7f57h25+tMO576fmq1X5iYdFU6uW5xuBp41Dm3zsxWxz//bIrj+pxzb8vxtURSq5/tWa6Z2DyFAUYF/XLN0ZaKTbs30T1/iB/Nr+LBuwZTBvxE+4UZt94SikZrE2qSNtIT6gDUnhl7rO+Y98f1s2PpygKVFuca8K8CLo1//ADwGKkDvkjhXH5H2u6aic1Ttp2KBfxyztGWiuTSy9emxsoyU0mkd4CiBv2x9fqdPX08/s9fZdmOf2JK30GPAH6U2HsXl/Q56T9OrD9BQYJ+rimdHudcQ9Lnx5xzZ6Y4bhD4L2AQWOec2+pxvpXASoBzzz334l/84hcTHptETIbumg7oHJ7G/ZM+xNuaV47MzMLW2jYqlm1ZRveJ2IYoS/YNcdPDjppB7+OLmt7Zu5mDD93ODHeYY+4NmEEDvwagwqO3X85y6AabU3tkM/tXIFVh7OeAB3wG/CbnXJeZzQV2Apc7536W7nWVw5cJyeJu3HK8y7JUdOzvoO3JNvqHYj3zl+wb4vd/DGf3utT9Uc2Y/8LzwQ1w1AQiaZYeGIO2nol9ZS45fOfc76Y58f+YWaNzrtvMGoFDHufoiv+938weAxYCaQO+yIRkSO+M3I274Lq0FTwK+IXVPDe2YL5p9yYOnjjI/ktmc/zjrdR/5ItUHxof6Fx8t6y85/NT5tjHpGICD/ak7xKbg1xz+NuADwPr4n9/f+wBZnYmcNI597qZTQOWAHfn+LoiqSXynuk2T4k3W1t8vIVOlo57ulzvsgyb5rnNI4E/Yc27vsx1WxmX3kku14Qc8/les/fkXHoxgnxCdW1s4lIAudbhrwOuMLOXgCvin2Nmi83s/vgx84FdZvZT4EfEcvgBvjeTyMl4Ny7Q+yrrJn2TFRWPj3tKFTzF0zHvV3zjSuPw1NQh1/X3c2jDxuxPvHdzLN3XVg8PrUyaDAQR2ONJqtqzYn8w74/rzyloA0B1y5TylWlvXKDTTWPJ6/eMfK4cfnElL+Y+eNeg54y0qqkpc3onkDx8/Ly1Z8U+DbjMMuWICliHLxJePtI7TXaEp2pauevU+9k19QpV6RRZ66LWkcXcCZVregb5XIN9isBehGCeK83wJRrUS79kdOzvYNPuTbzpPzv5+COOSQPeMaqqqYl5G28q0Ew+fq76c0oqsOdUllksCviSVz7SO0DJ/ecuJ6nuiXj3gd0c2rAx5cYpMY751x8ktyBfHrP3BAV8Ech4c9YIzfYDl+meCK/dssBRNWWIGQt+Rf2cbKqrSnP27od2vBIBf9U7oJ2ziiBTV9MZt96CTRrfXROMwZNVdD9dT+8rmaqr4tUy9efANfdBW2/s56GMgn0mCvgSPZm2SgRtjB4wr3sfFh//IWy4kPpnPkTjokNUTRkkVfrGDVVwaO8ZKc6gIJ9MVToSPX5uzoKCN7KS05oaavmf4SeZPH07FdU9zBgc5pajR2k+eRJ6Y8fUz+mjfk4fLzyYevOUwZOVvLRtxun0Thmma3KlGb5EUyK9c81fZ94Y/aGPabZfYB940w9oaHyQikk9YHCouoK108/ikbop446tmjKU4gwwkt7ZPYPei/8+0jN5Lwr4Em1+NkaHUdsmSp7t3cwPjv8Lr49pPdlfUcGmMxvGHT5jwa+wymHP07lTA+PuxtW+xjEK+CLZLOZqtp8/iXYHD32Mg5WpQ9HBqspxj9XP6aPxf/dSVec10z+9eUpve7v2NU6igC+S4GcxFzTbz4VHT5uZg6mD9/jHY+8C6v/XNOb9zRepamryfKnE3bg/+erfaV/jOAV8kQS/6R1Q6WY2fDQuaz3WQ83w6DRNzfAwrcd6SFdpM+PWW7CaGs+Xdv39rNg1rokvEM2uqAr4Isn8LuaCSjf9SNzhnKE7ZfOJk7QdOUrjwCDmHI0Dg7QdOUpz1dme5ZRb93Ry5b46/uKC93Gk7kzPe21n9PXw7e1f5NJXnxn1eBS7oupOWxEvfu/MzeGuzbLdYtH39y4FH9/HrXs6uX3HA9hZj2DVPbiBBr769T5mnDjh+TX9ldVsetu1PHbOxWXdFVXdMkUmYsF1sT8Z+/DEJ01Z1u2n2hh7zUPPApRmIMq1HXEWLS2+9ON/oGLGFqxiAACb1MN33218/F8qmeyxHlAzNMBHnn+Ely5aWj6/WLOklI5IJtnm9n1W8mRqJ1ASct5YJCk/n0X/opN17SPBPuGJixxfW1abdiH3N/p6+PaOL/HuA7t9jq+8aIYv4kditp+pzXKCj9m+16Jh6BcTc+45n3vjsorq1Bt8P7mgn3lfeTpNs7U8bpVYgjTDF8mG39JNyDjb91o0DOViYr62CMxTT5v6STPSPu6nemdCWyWWOC3aimRrArnqxBGdw9O4f9KHeFvzSoC0LYHHCnyBN59bBOa55XTH/g4+//gXGHCvn34Jm8ydS/98ZGP03vb2DL30fW6VWGLUD1+kUCZQjTLsYuGzb0oj++bfyi3Pz8sYxDP1i8+bvO4DW9ie84mdsQ6eOMjMupm0LmodCfbJ0qV3AKymhsY715ZN0FfAFyk0vztqjeMvKC5Zt5POFLn9WQ21PLH6suzHm6yEgvxE9La30/35O3D9/Z7HVDU1MW/nowGOqnBUlilSYFuHlvBf7iZuHP57ZlUcwTJ/SVxSSedDK2M5/xTBMu8LvHnd7Du/QT7fqavEzD1deifRe6fc0jtjaYYvkqOx6ZYVFY/zF9X3U2uncjjr6H1Wh/uO0TV8NncPXse24aUjR2Wc4Y8E9gNQe2bssb6jhHUmX+jUVRTSO0rpiBRQqnTLiorHuX3SP/IbHMY5qPA/5U8rkf/vdNP4MQt5X91zTOk7mBTMj+U5sCcrfLqmoKkropHeUUpHpIBSpVW2DS+lvX8pTQ21XHz8h9xWtZkmOwLkFvwTXzvbjnADP8QSL9139PRByR/nHOyDzckX6t6E5AXe5t87gw/+pIbqQ6lr+QuV3vG7yFxICvgiOWpqqE05K21qqKWrp49OlrLtVCwNs6Li8ZHgX2G5zb7z9KbB48zFWXhN972cqI79HbQ92Ub/UGxW/4N5x/nXt9Twzfsa0gb9fN6cNXYM3Se6aXuyDSDQoK8br0RytGr5+dRWj96oo7a6klXLzx8XqLYNL2XpqXv47dp/jt2ANNKuoXDh259wbPad7ns5UZt2bxoJtAn9Q/18910Vgd2c5TWGTbs35eX8fingi+To6oWzuOuai5jVUIsRyzcnFhnTBrBEK+a23hecdFEAAAkdSURBVCIF/3AE+WTpvpcTdfDEwZSPd8z7FQc//T6O1Jvn+6zknbNy4TUGr8cLRYu2IgWWdZlh3itrRlf80HcM6meHpk6+0JZtWUb3ie5xjzfWNQKx9Mq99w4y/bj3OXKt3kk3hh3X7pjQOb2oSkekHCT/IqifDfOWwUs7xvxiODb64wgFdi9j8+cANZU1tL2zjTX/tgaHY8m+IW562FEz6H0eBxyrr+Tvf8fxs0tmjSy6+lmMTTeGfOfwFfBFJNK8gnLyzHvJviFueMwx7Xj6pFp/FXzjSuOZBXVc9ZtX8f2Xv+8rkAdVpaOALxKQst3Bqkylmnl/9atDTOtNHxcPT4Wbb66iwioYdsPjni9EqsavdAE/p0VbM3u/me0zs2EzS/kC8ePeY2YvmtnLZrY6l9cUCavEXaKdPX04Tu9gtXVPZ7GHFkpb93SyZN1OzlvdwZJ1O4vyfWqe20zbO9torGvEMBrrGhlaeX3a6h2Aacfh3nsHecdzAymfD3ox1q9c6/CfA64BvuF1gJlVAvcCVwAHgKfNbJtz7vkcX1skVNLtYKVZ/mhh2t6xeW7zuNRK74yFaXvvGDD9OHz8YQcM8cQFoyuxZtbNLNBoc5PTDN8594JzLtN+bJcALzvn9jvnTgEPAlfl8roiYRSmHay27unkbX++gzmrO5izuoOFa3eE6p2G1y/Htm37ijSi0epbWpi381Ga1t+ddrY/eRA+s81x772DLNkXu56ayhpaF7UGNdSsBFGHPwtIbhZ+IP7YOGa20sx2mdmuw4cPBzA0kfwJyw5WW/d0suoff0pP3+l0w7GTA6za8tPQBH2vX4I9fQOhGSPEAn/jnWupamryLIhNzPZvetjxey9NLUjlTb5kDPhm9q9m9lyKP35n6akWvFN+75xz9znnFjvnFk+fPt3n6UXCoRB3iU7E+u0vMjA8/r/YwJALzQbp6X4JhmWMCYnZfnWazdEBagbhD7cc5c03fiXnG7UKJWPAd879rnPuwhR/vu/zNQ4A5yR9Phvw7k8qUqIKcZfoRKRLIYVlg/R0vwTDMsaxMu2Tm5DowxPGoB9E87SngXlmdh7QCVwP3BDA64oE7uqFs4q+QOvVgCzxXC7yVXZ69cJZ/Hn7Po6dHF/lEspN3PG3kUqC6++na9VtHNqwMVSbquRalvk+MzsAvAPoMLPt8cebzOxhAOfcIPApYDvwArDZOReOlRmRMrRq+flUp+jBXF1pOaWX8l12+oWWC0KRAsuG38XchLDN9nXjlUgZ2rqnk7Zt+0YWbs+cUs0XWi7I6d1HITYnKeUb1Xrb233N9iG2aFnd1BTIbF932opIzs5b3ZGy2sKAn68LZ1VKEPzsopUQxBaKBbvTVkSiIyxlp2GTXLqZSSK3n4+WyxOhgC8ivoSl7DSMknP7/T5KYYqV21fAFxFfwlJ2Gmb1LS1svvosDk/NvHNBMWb72tNWRHwLQ9lp2C3949v507e0cfHeExl77EP+989NRzN8EZE8SnTg3H/JbO67soKj9ZWhme2rSkdEpMCCrORRlY6ISBFlW8lzaMPGgoxDAV9EJADZ3KU72D1+w/N80KKtiEiA/PTkqWpsLMhra4YvIhKwdLN9q6lhxq23FOR1NcMXESmSUbP97m6qGhsL2m9HAV9EpIjqW1oCa5+slI6ISEQo4IuIRIQCvohIRCjgi4hEhAK+iEhEKOCLiESEAr6ISEQo4IuIRERo2yOb2WHgF3k63TTgSJ7OFXZRulbQ9ZazKF0r5O963+icm57qidAG/Hwys11e/aHLTZSuFXS95SxK1wrBXK9SOiIiEaGALyISEVEJ+PcVewABitK1gq63nEXpWiGA641EDl9ERKIzwxcRiTwFfBGRiCibgG9m7zGzF83sZTNbneL5yWb2vfjz/2Fmc4IfZf74uN4/MbPnzWyvmT1qZm8sxjjzJdP1Jh13rZk5MyvZcj4/12pm18X/ffeZ2XeCHmM++fhZPtfMfmRme+I/z1cWY5z5YGbfMrNDZvacx/NmZvfEvxd7zWxRXgfgnCv5P0Al8DNgLjAJ+Cnw1jHHfBL4evzj64HvFXvcBb7edwNT4h9/otyvN37cGcBPgKeAxcUedwH/becBe4Az45/PKPa4C3y99wGfiH/8VuCVYo87h+t9F7AIeM7j+SuBRwAD3g78Rz5fv1xm+JcALzvn9jvnTgEPAleNOeYq4IH4x1uAy83MAhxjPmW8Xufcj5xzJ+OfPgXMDniM+eTn3xfgTuBuoD/IweWZn2v9GHCvc+4YgHPuUMBjzCc/1+uAqfGP64GuAMeXV865nwBH0xxyFfC3LuYpoMHMGvP1+uUS8GcBryZ9fiD+WMpjnHODQC9wdiCjyz8/15vso8RmDaUq4/Wa2ULgHOfcD4IcWAH4+bd9M/BmM3vCzJ4ys/cENrr883O9bcCHzOwA8DDw6WCGVhTZ/t/OSrlsYp5qpj623tTPMaXC97WY2YeAxcDvFHREhZX2es2sAtgAfCSoARWQn3/bKmJpnUuJvXP7NzO70DnXU+CxFYKf6/0g8G3n3F+a2TuAv4tf73Dhhxe4gsapcpnhHwDOSfp8NuPf9o0cY2ZVxN4apntrFWZ+rhcz+13gc8AK59zrAY2tEDJd7xnAhcBjZvYKsdznthJduPX7s/x959yAc+7nwIvEfgGUIj/X+1FgM4Bz7t+BGmKNxsqRr//bE1UuAf9pYJ6ZnWdmk4gtym4bc8w24MPxj68Fdrr4KkkJyni98RTHN4gF+1LO8UKG63XO9Trnpjnn5jjn5hBbs1jhnNtVnOHmxM/P8lZii/KY2TRiKZ79gY4yf/xc7y+BywHMbD6xgH840FEGZxvwh/FqnbcDvc657nydvCxSOs65QTP7FLCd2Kr/t5xz+8xsLbDLObcN+Caxt4IvE5vZX1+8EefG5/WuB94A/GN8bfqXzrkVRRt0Dnxeb1nwea3bgWVm9jwwBKxyzr1WvFFPnM/r/VPgr83sVmLpjY+U6mTNzL5LLBU3Lb4m8QWgGsA593ViaxRXAi8DJ4E/yuvrl+j3TUREslQuKR0REclAAV9EJCIU8EVEIkIBX0QkIhTwRUQiQgFfRCQiFPBFRCLi/wOEv/8x4x7BfQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "RM.save_RM('RM4')\n", + "RM_back4 = mwinai.manage_RM(RM_filename='RM4')\n", + "RM_back4.set_test(X_test, scaleit=True)\n", + "RM_back4.predict(scoring=False)\n", + "plt.scatter(X_train, y_train)\n", + "plt.scatter(RM_back4.X_test_unscaled, RM_back4.pred[:,0])\n", + "plt.scatter(X_train, y_train2)\n", + "plt.scatter(RM_back4.X_test_unscaled, RM_back4.pred[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw------- 1 christophemorisset staff 333K Sep 26 20:16 RM1.mwinai_sk\n", + "-rw------- 1 christophemorisset staff 336K Sep 26 20:18 RM2.mwinai_sk\n", + "-rw------- 1 christophemorisset staff 679B Sep 26 20:34 RM3.mwinai_k0\n", + "-rw------- 1 christophemorisset staff 149K Sep 26 20:34 RM3.mwinai_k1\n", + "-rw------- 1 christophemorisset staff 679B Sep 26 20:36 RM4.mwinai_k0\n", + "-rw------- 1 christophemorisset staff 149K Sep 26 20:36 RM4.mwinai_k1\n" + ] + } + ], + "source": [ + "!ls -lh RM*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/mwinai/Regressor/RegressionModel.py b/mwinai/Regressor/RegressionModel.py index 7133c09..d2d2d72 100644 --- a/mwinai/Regressor/RegressionModel.py +++ b/mwinai/Regressor/RegressionModel.py @@ -10,6 +10,7 @@ import numpy as np import time import random +from glob import glob from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, RobustScaler from sklearn.externals import joblib @@ -65,7 +66,8 @@ def __init__(self, RM_type = 'SK_ANN', use_log=False, split_ratio = None, verbose=False, - RM_filename=None, use_RobustScaler=False, + RM_filename=None, + use_RobustScaler=False, random_seed=None, noise=None, N_y_bins=None, @@ -713,15 +715,23 @@ def save_RM(self, filename='RM', save_train=False, save_test=False): self.N_test, self.N_test_y, self.N_train, self.N_train_y, self.pca_N, self.pca, self.training_time, self.random_seed, self.noise, X_train, y_train, X_test, y_test, - self.train_scaled, self.test_scaled, self.verbose), filename+'.mwinai1') + self.train_scaled, self.test_scaled, self.verbose), filename+'.mwinai_sk') elif self.RM_type[0:2] == 'K_': - pass - + joblib.dump((self.RM_type, self.RM_version, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y, + self.pca_N, self.pca, self.training_time, self.random_seed, + self.noise, X_train, y_train, X_test, y_test, + self.train_scaled, self.test_scaled, self.verbose), filename+'.mwinai_k0') + for i, RM in enumerate(self.RMs): + RM.save('{}.mwinai_k{}'.format(filename, i+1)) + if self.verbose: print('RM save to {}'.format(filename)) - def load_RM(self, filename='RM_jl.sav'): + def load_RM(self, filename='RM'): """ Loading previously saved model. joblib is used to load. @@ -739,56 +749,77 @@ def load_RM(self, filename='RM_jl.sav'): RM.predict(scoring=False) """ - - RM_tuple = joblib.load(filename) - if self.RM_version != RM_tuple[1] and self.verbose: - print('WARNING: version loaded from {} is {}. Version from RM class is {}.'.format(filename, - RM_tuple[1],self.RM_version)) - if RM_tuple[1] in ("0.15"): - (self.RM_type, self.RM_version, self.RMs, - self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, - self.N_in, self.N_out, self.N_in_test, self.N_out_test, - self.N_test, self.N_test_y, self.N_train, self.N_train_y, - self.pca_N, self.pca, self.training_time, self.random_seed, - self.noise, self.X_train, self.y_train, self.X_test, self.y_test, - self.train_scaled, self.test_scaled, self.verbose) = RM_tuple - elif RM_tuple[1] in ("0.14"): - (self.RM_type, self.RM_version, self.RMs, - self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, - self.N_in, self.N_out, self.N_in_test, self.N_out_test, - self.N_test, self.N_test_y, self.N_train, self.N_train_y, - self.pca_N, self.pca, self.training_time, self.random_seed, self.noise) = RM_tuple - elif RM_tuple[1] in ("0.12", "0.13"): - (self.RM_type, self.RM_version, self.RMs, - self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, - self.N_in, self.N_out, self.N_in_test, self.N_out_test, - self.N_test, self.N_test_y, self.N_train, self.N_train_y, - self.pca_N, self.pca, self.training_time, self.random_seed) = RM_tuple - elif RM_tuple[1] == "0.11": - (self.RM_type, self.RM_version, self.RMs, - self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, - self.N_in, self.N_out, self.N_in_test, self.N_out_test, - self.N_test, self.N_test_y, self.N_train, self.N_train_y, - self.pca_N, self.pca, self.training_time) = RM_tuple - elif RM_tuple[1] == "0.10": - (self.RM_type, self.RM_version, self.RMs, - self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, - self.N_in, self.N_out, self.N_in_test, self.N_out_test, - self.N_test, self.N_test_y, self.N_train, self.N_train_y, - self.pca_N, self.pca) = RM_tuple - elif "{:.1f}".format(RM_tuple[1]) == "0.9": - (self.RM_type, self.RM_version, self.RMs, - self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, - self.N_in, self.N_out, self.N_in_test, self.N_out_test, - self.N_test, self.N_test_y, self.N_train, self.N_train_y) = RM_tuple - elif "{:.1f}".format(RM_tuple[1]) == "0.8": - (self.RM_type, self.RM_version, self.RMs, - self.scaler, self.train_score, self._multi_predic, - self.N_in, self.N_out, self.N_in_test, self.N_out_test, - self.N_test, self.N_test_y, self.N_train, self.N_train_y) = RM_tuple - self.trained = True - if self.verbose: - print('RM loaded from {}'.format(filename)) + files = glob("{}.*".format(filename)) + if "{}.mwinai_sk".format(filename) in files: + RM_tuple = joblib.load("{}.mwinai_sk".format(filename)) + if self.RM_version != RM_tuple[1] and self.verbose: + print('WARNING: version loaded from {} is {}. Version from RM class is {}.'.format(filename, + RM_tuple[1],self.RM_version)) + if RM_tuple[1] in ("0.15"): + (self.RM_type, self.RM_version, self.RMs, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y, + self.pca_N, self.pca, self.training_time, self.random_seed, + self.noise, self.X_train, self.y_train, self.X_test, self.y_test, + self.train_scaled, self.test_scaled, self.verbose) = RM_tuple + elif RM_tuple[1] in ("0.14"): + (self.RM_type, self.RM_version, self.RMs, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y, + self.pca_N, self.pca, self.training_time, self.random_seed, self.noise) = RM_tuple + elif RM_tuple[1] in ("0.12", "0.13"): + (self.RM_type, self.RM_version, self.RMs, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y, + self.pca_N, self.pca, self.training_time, self.random_seed) = RM_tuple + elif RM_tuple[1] == "0.11": + (self.RM_type, self.RM_version, self.RMs, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y, + self.pca_N, self.pca, self.training_time) = RM_tuple + elif RM_tuple[1] == "0.10": + (self.RM_type, self.RM_version, self.RMs, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y, + self.pca_N, self.pca) = RM_tuple + elif "{:.1f}".format(RM_tuple[1]) == "0.9": + (self.RM_type, self.RM_version, self.RMs, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y) = RM_tuple + elif "{:.1f}".format(RM_tuple[1]) == "0.8": + (self.RM_type, self.RM_version, self.RMs, + self.scaler, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y) = RM_tuple + self.trained = True + if self.verbose: + print('RM loaded from {}.mwinai_sk'.format(filename)) + + elif "{}.mwinai_k0".format(filename) in files: + RM_tuple = joblib.load("{}.mwinai_k0".format(filename)) + if self.RM_version != RM_tuple[1] and self.verbose: + print('WARNING: version loaded from {} is {}. Version from RM class is {}.'.format(filename, + RM_tuple[1],self.RM_version)) + if RM_tuple[1] in ("0.15"): + (self.RM_type, self.RM_version, + self.scaler, self.scaler_y, self.scaling_y, self.train_score, self._multi_predic, + self.N_in, self.N_out, self.N_in_test, self.N_out_test, + self.N_test, self.N_test_y, self.N_train, self.N_train_y, + self.pca_N, self.pca, self.training_time, self.random_seed, + self.noise, self.X_train, self.y_train, self.X_test, self.y_test, + self.train_scaled, self.test_scaled, self.verbose) = RM_tuple + if self.verbose: + print('RM loaded from {}.mwinai_k0'.format(filename)) + self.RMs = [load_model("{}.mwinai_k1".format(filename))] + self.trained = True + if self.verbose: + print('RM loaded from {}.mwinai_k1'.format(filename)) #%% if __name__ == "__main__": diff --git a/mwinai/version.py b/mwinai/version.py index f9e8a2f..c125e34 100644 --- a/mwinai/version.py +++ b/mwinai/version.py @@ -1,4 +1,4 @@ # -*- coding: utf-8 -*- # tmniai version -__version__="0.2.1" +__version__="0.2.2b2" From f484092b1531438bbbc8d32af9fb647e135b0df6 Mon Sep 17 00:00:00 2001 From: morisset Date: Thu, 26 Sep 2019 20:44:04 -0700 Subject: [PATCH 2/4] Update .gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 74cc348..434d87b 100644 --- a/.gitignore +++ b/.gitignore @@ -54,3 +54,6 @@ nosetests.xml coverage.xml *.cover +*.mwinai_sk +*.mwinai_k0 +*.mwinai_k1 From a56b1172d32e47422ad917be7d39d3e87d661d8e Mon Sep 17 00:00:00 2001 From: morisset Date: Thu, 26 Sep 2019 20:55:50 -0700 Subject: [PATCH 3/4] Scoring in try --- mwinai/Regressor/RegressionModel.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/mwinai/Regressor/RegressionModel.py b/mwinai/Regressor/RegressionModel.py index d2d2d72..81dcc0d 100644 --- a/mwinai/Regressor/RegressionModel.py +++ b/mwinai/Regressor/RegressionModel.py @@ -632,14 +632,19 @@ def predict(self, scoring=True, reduce_by=None): if self.N_test != self.N_test_y: raise Exception('N_test {} != N_test_y {}'.format(self.N_test, self.N_test_y)) if self._multi_predic: - self.predic_score = [RM.score(self.X_test, self.y_test) for RM in self.RMs] + try: + self.predic_score = [RM.score(self.X_test, self.y_test) for RM in self.RMs] + except: + self.predic_score = [np.nan for RM in self.RMs] else: if self.y_train.ndim == 1: y_tests = (self.y_test,) else: y_tests = self.y_test.T - - self.predic_score = [RM.score(self.X_test, y_test) for RM, y_test in zip(self.RMs, y_tests)] + try: + self.predic_score = [RM.score(self.X_test, y_test) for RM, y_test in zip(self.RMs, y_tests)] + except: + self.predic_score = [np.nan for RM, y_test in zip(self.RMs, y_tests)] if self.N_out != self.N_out_test: raise Exception('N_out {} != N_out_test {}'.format(self.N_out, self.N_out_test)) From 059f3ea241ecdd86243c80126083195ec93be890 Mon Sep 17 00:00:00 2001 From: morisset Date: Thu, 26 Sep 2019 20:56:53 -0700 Subject: [PATCH 4/4] 0.2.2 --- mwinai/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mwinai/version.py b/mwinai/version.py index c125e34..58b16e2 100644 --- a/mwinai/version.py +++ b/mwinai/version.py @@ -1,4 +1,4 @@ # -*- coding: utf-8 -*- # tmniai version -__version__="0.2.2b2" +__version__="0.2.2"