Merge pull request #3 from chris-krokus/ML_Models

Ml models
UncelSoogar · Sep 14, 2020 · f2d6728 · f2d6728
2 parents 573172f + d82338e
commit f2d6728
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diff --git a/Notebooks/.ipynb_checkpoints/cat_ml-checkpoint.ipynb b/Notebooks/.ipynb_checkpoints/cat_ml-checkpoint.ipynb
diff --git a/Notebooks/.ipynb_checkpoints/dog_ml-checkpoint.ipynb b/Notebooks/.ipynb_checkpoints/dog_ml-checkpoint.ipynb
diff --git a/Notebooks/.ipynb_checkpoints/model_testing-checkpoint.ipynb b/Notebooks/.ipynb_checkpoints/model_testing-checkpoint.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in cat model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import cat scaler\n",
+    "filename = '../App/Models/cat_scaler.sav'\n",
+    "infile = open(filename,'rb')\n",
+    "cat_scaler = pickle.load(infile)\n",
+    "infile.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import cat scaler\n",
+    "filename = '../App/Models/cat_model.sav'\n",
+    "infile = open(filename,'rb')\n",
+    "cat_model = pickle.load(infile)\n",
+    "infile.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
+      "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
+      "                           max_features=None, max_leaf_nodes=None,\n",
+      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
+      "                           min_samples_leaf=1, min_samples_split=2,\n",
+      "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
+      "                           n_iter_no_change=None, presort='auto',\n",
+      "                           random_state=None, subsample=1.0, tol=0.0001,\n",
+      "                           validation_fraction=0.1, verbose=0,\n",
+      "                           warm_start=False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Cat cols\n",
+    "Index(['Length of Stay(days)', 'Animal Breed_DOMESTIC LH',\n",
+    "       'Animal Breed_DOMESTIC MH', 'Animal Breed_DOMESTIC SH',\n",
+    "       'Animal Origin_FIELD', 'Animal Origin_OVER THE COUNTER',\n",
+    "       'Chip Status_CHIP', 'Chip Status_NO CHIP', 'Chip Status_UNABLE TO SCAN',\n",
+    "       'Intake Condition_CONTAGIOUS', 'Intake Condition_HEALTHY',\n",
+    "       'Intake Condition_MANAGEABLE NON-CONTAGIOUS',\n",
+    "       'Intake Condition_REHABILITABLE NON-CONTAGIOUS',\n",
+    "       'Intake Condition_UNTREATABLE NON-CONTAGIOUS'],"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Conjure a cat\n",
+    "a_cat = [[1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Scale the cat\n",
+    "a_cat = cat_scaler.transform(a_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.09168482, 0.60837809, 0.00141407, 0.29852302]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_model.predict_proba(a_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_model.predict(a_cat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Repeat for dog model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import dog scaler\n",
+    "filename = '../App/Models/dog_scaler.sav'\n",
+    "infile = open(filename,'rb')\n",
+    "dog_scaler = pickle.load(infile)\n",
+    "infile.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import dog scaler\n",
+    "filename = '../App/Models/dog_model.sav'\n",
+    "infile = open(filename,'rb')\n",
+    "dog_model = pickle.load(infile)\n",
+    "infile.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Conjure a dog\n",
+    "a_corgi = [[1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0]]\n",
+    "a_pug =   [[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Dog cols\n",
+    "Index(['Length of Stay(days)', 'Animal Breed_CHIHUAHUA', 'Animal Breed_CORGI',\n",
+    "       'Animal Breed_HOUND', 'Animal Breed_OTHER', 'Animal Breed_PIT BULL',\n",
+    "       'Animal Breed_PUG', 'Animal Breed_RETRIEVER', 'Animal Breed_SHEPHERD',\n",
+    "       'Animal Breed_TERRIER', 'Animal Origin_FIELD',\n",
+    "       'Animal Origin_OVER THE COUNTER', 'Chip Status_CHIP',\n",
+    "       'Chip Status_NO CHIP', 'Chip Status_UNABLE TO SCAN',\n",
+    "       'Intake Condition_CONTAGIOUS', 'Intake Condition_HEALTHY',\n",
+    "       'Intake Condition_MANAGEABLE NON-CONTAGIOUS',\n",
+    "       'Intake Condition_REHABILITABLE NON-CONTAGIOUS',\n",
+    "       'Intake Condition_UNTREATABLE NON-CONTAGIOUS'],\n",
+    "      dtype='object')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Scale the dogs\n",
+    "a_corgi = dog_scaler.transform(a_corgi)\n",
+    "a_pug = dog_scaler.transform(a_pug)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.47655399, 0.04513255, 0.22901109, 0.24930237]])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dog_model.predict_proba(a_corgi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.37818874, 0.02501556, 0.3313329 , 0.2654628 ]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dog_model.predict_proba(a_pug)"
+   ]
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}