Changes

Author: VectorZhao

DNN_pred_k2_20220924.h5

@@ -15,7 +15,7 @@
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.ticker as tck\n",
     "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
-    "import seaborn as sns\n",
+    "# import seaborn as sns\n",
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -26,7 +26,7 @@
     "import mdn\n",
     "import joblib\n",
     "\n",
-    "from sklearn.model_selection import train_test_split\n",
+    "# from sklearn.model_selection import train_test_split\n",
     "\n",
     "import matplotlib\n",
     "matplotlib.rcParams['svg.fonttype'] = 'none'\n",
@@ -53,7 +53,24 @@
     },
     "id": "OHSSb5g_iWsK"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
+      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_19692\\2450612377.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     22\u001B[0m     \u001B[0mcompile\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     23\u001B[0m )\n\u001B[1;32m---> 24\u001B[1;33m \u001B[0minput_scaler\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mjoblib\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"MDN_MRFe(Mg+Si)k2_Xscaler_20220917.save\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     25\u001B[0m \u001B[0moutput_scaler\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mjoblib\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"MDN_MRFe(Mg+Si)k2_yscaler_20220917.save\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\site-packages\\joblib\\numpy_pickle.py\u001B[0m in \u001B[0;36mload\u001B[1;34m(filename, mmap_mode)\u001B[0m\n\u001B[0;32m    603\u001B[0m                     \u001B[1;32mreturn\u001B[0m \u001B[0mload_compatibility\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfobj\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    604\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 605\u001B[1;33m                 \u001B[0mobj\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_unpickle\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfobj\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mfilename\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmmap_mode\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    606\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    607\u001B[0m     \u001B[1;32mreturn\u001B[0m \u001B[0mobj\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\site-packages\\joblib\\numpy_pickle.py\u001B[0m in \u001B[0;36m_unpickle\u001B[1;34m(fobj, filename, mmap_mode)\u001B[0m\n\u001B[0;32m    527\u001B[0m     \u001B[0mobj\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    528\u001B[0m     \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 529\u001B[1;33m         \u001B[0mobj\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0munpickler\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    530\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0munpickler\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcompat_mode\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    531\u001B[0m             warnings.warn(\"The file '%s' has been generated with a \"\n",
+      "\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\pickle.py\u001B[0m in \u001B[0;36mload\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m   1086\u001B[0m                     \u001B[1;32mraise\u001B[0m \u001B[0mEOFError\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1087\u001B[0m                 \u001B[1;32massert\u001B[0m \u001B[0misinstance\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mbytes_types\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1088\u001B[1;33m                 \u001B[0mdispatch\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1089\u001B[0m         \u001B[1;32mexcept\u001B[0m \u001B[0m_Stop\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mstopinst\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1090\u001B[0m             \u001B[1;32mreturn\u001B[0m \u001B[0mstopinst\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mvalue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\pickle.py\u001B[0m in \u001B[0;36mload_global\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m   1374\u001B[0m         \u001B[0mmodule\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreadline\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"utf-8\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1375\u001B[0m         \u001B[0mname\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreadline\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"utf-8\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1376\u001B[1;33m         \u001B[0mklass\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfind_class\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mname\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1377\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mklass\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1378\u001B[0m     \u001B[0mdispatch\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mGLOBAL\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mload_global\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\pickle.py\u001B[0m in \u001B[0;36mfind_class\u001B[1;34m(self, module, name)\u001B[0m\n\u001B[0;32m   1424\u001B[0m             \u001B[1;32melif\u001B[0m \u001B[0mmodule\u001B[0m \u001B[1;32min\u001B[0m \u001B[0m_compat_pickle\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mIMPORT_MAPPING\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1425\u001B[0m                 \u001B[0mmodule\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_compat_pickle\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mIMPORT_MAPPING\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1426\u001B[1;33m         \u001B[0m__import__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlevel\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1427\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mproto\u001B[0m \u001B[1;33m>=\u001B[0m \u001B[1;36m4\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1428\u001B[0m             \u001B[1;32mreturn\u001B[0m \u001B[0m_getattribute\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0msys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmodules\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mname\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'sklearn'"
+     ]
+    }
+   ],
    "source": [
     "input_parameters = [\n",
     "    'Mass', \n",
@@ -814,7 +831,7 @@
   },
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

MDN_two_planets_prediction.ipynb

@@ -21,7 +21,7 @@ We provide two machine learning models for uses: **Model A** trained on `[M, R,
 Download [`Anaconda`](https://www.anaconda.com/products/individual#Downloads) and install it on your machine.
 Create a `conda` environment called `Rocky_Exoplanets` and install all the necessary dependencies:
 
-    $ conda create -n Rocky_Exoplanets pip python=3.7.6 keras-mdn-layer jupyter
+    $ conda create -n Rocky_Exoplanets pip python=3.7.6 jupyter
 
 ### Step 3:
 Activate the `Rocky_Exoplanets` environment:
@@ -46,12 +46,15 @@ Open `Jupyter Notebook` and load the file `MDN_two_planets_prediction.ipynb`:
 At this point you are ready to start investigating the interiors of rocky exoplanets!
 
 ## Usage
+
 ```python
-from deepexo import RockyPlanet
+from deepexo import rockyplanet
+
 # Kepler-78b
-M = 1.77 # mass in Earth masses
-R = 1.228 # radius in Earth radii
-cFeMg = 0.685 # bulk Fe/(Mg + Si) (molar ratio)
+M = 1.77  # mass in Earth masses
+R = 1.228  # radius in Earth radii
+cFeMg = 0.685  # bulk Fe/(Mg + Si) (molar ratio)
+
 
 ```
 ## References

README.md

@@ -1 +0,0 @@
-from deepexo.rockyplanet import PlanetSynth

deepexo/__init__.py

@@ -3,18 +3,31 @@
 
 import mdn
 import joblib
-from numpy.typing import ArrayLike
-class RockyPlanet:
-    def predict(self, planet_params: ArrayLike):
-        """
-        Args:
-            Array of floats of the planet parameters
-            in the following order: [M, R, k2].
-            The following input ranges are supported:
-                M: 0.1 < M [M_earth] < 30
-                R: 0.1 < R [R_earth] < 30
-                k2: 0 < k2 < 0.5
-        Returns:
 
-        """
+OUTPUT_DIMS = 6  # 6 outputs:
+# 'H2O_radial_frac', 'Mantle_radial_frac', 'Core_radial_frac', 'Core_mass_frac', 'P_CMB', 'T_CMB',
+N_MIXES = 20  # 20 mixtures
+
+
+class RockyPlanet:
+    def __init__(self):
+        self.model_a = load_model(r"model/model_a.h5", custom_objects={
+            'MDN': mdn.MDN(OUTPUT_DIMS, N_MIXES),
+            "mdn_loss_func": mdn.get_mixture_loss_func(OUTPUT_DIMS, N_MIXES)}, compile=False)
+        self.model_a_scaler = joblib.load(r"model/model_a_scaler.save")
+        self.model_b = load_model(r"model/model_b.h5", custom_objects={
+            'MDN': mdn.MDN(OUTPUT_DIMS, N_MIXES), "mdn_loss_func": mdn.get_mixture_loss_func(OUTPUT_DIMS, N_MIXES)})
+        self.model_b_scaler = joblib.load(r"model/model_b_scaler.save")
 
+    def predict(self, planet_params):
+        if len(planet_params) == 3:
+            print("正在调用inputs={}的model A进行预测".format(planet_params))
+            scaled_params = self.model_a_scaler.transform(np.array(planet_params).reshape(1, -1))
+            return self.model_a.predict(scaled_params)[0]
+        elif len(planet_params) == 4:
+            print("正在调用inputs={}的model B进行预测".format(planet_params))
+            scaled_params = seyhlf.model_b_scaler.transform(np.array(planet_params).reshape(1, -1))
+            return self.model_b.predict(scaled_params)[0]
+        else:
+            raise ValueError(
+                "Invalid number of planet parameters. Expected 3 or 4, but got {}".format(len(planet_params)))

deepexo/model/model_b.h5

@@ -1,7 +1,11 @@
+hdf5==1.10.4
+h5py==2.10.0
 joblib==0.14.1
+tensorflow==2.3.0
+scikit-learn==0.24.0
 tensorflow_probability==0.11.0
+tensorflow-estimator==2.3.0
 matplotlib==3.1.3
-tensorflow==2.3.0
 numpy==1.18.0
 pandas==1.2.3
-xlrd==1.2.0
+xlrd==1.2.0

DNN_pred_k2_Xscaler_20220924.save renamed to deepexo/model/model_b_scaler.save

@@ -0,0 +1,10 @@
+from deepexo.rockyplanet import RockyPlanet
+
+M = 1.77,  # mass in Earth masses
+R = 1.228,  # radius in Earth radii
+cFeMg = 0.685,  # bulk Fe/(Mg + Si) (molar ratio)
+k2 = 0.819, # tide Love number
+
+planet_params = [M, R, cFeMg, k2]
+rp = RockyPlanet()
+pred = rp.predict(planet_params)