load_model_AB5_Consulting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "from sklearn import model_selection\n",
    "import sklearn as read_csv\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import numpy as np\n",
    "from random import shuffle\n",
    "from operator import itemgetter\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "from sklearn import model_selection\n",
    "import sklearn as read_csv\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import numpy as np\n",
    "from random import shuffle\n",
    "from operator import itemgetter\n",
    "from sklearn import preprocessing\n",
    "from gplearn.genetic import SymbolicRegressor\n",
    "from sklearn.utils.random import check_random_state\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from mlxtend.plotting import plot_learning_curves\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "import itertools\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from matplotlib import pyplot\n",
    "#We choose 5 cross validation for our machine learning model\n",
    "n_folds = KFold(n_splits =5,shuffle= False )\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import jaccard_score\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense,Activation\n",
    "from keras.utils import np_utils\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold # evaluate a given model using cross-validation\n",
    "\n",
    "# import packages for hyperparameters tuning\n",
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "\n",
    "import keras\n",
    "import keras.utils\n",
    "from keras import utils as np_utils\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from keras.layers import Dropout\n",
    "# Example of Dropout on the Sonar Dataset: Hidden Layer\n",
    "from pandas import read_csv\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.constraints import maxnorm\n",
    "from keras.optimizers import SGD\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "import pickle\n",
    "import random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import loadtxt\n",
    "from keras.models import load_model\n",
    " \n",
    "# load model\n",
    "model = load_model('model_rwet_dry.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames=['date','time','voltage','humidity','target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(\"sensor_readings.csv\",names=colnames, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded=df1[['date','time','target']].apply(LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "remain=df1[['voltage','humidity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding both the dataframes encoded and remaining (without encoding)\n",
    "data =pd.concat([remain,encoded], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[['voltage', 'humidity', 'date', 'time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       ...,\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\couli\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass threshold=0.5 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import binarize\n",
    "\n",
    "#everything together \n",
    "predict = np.ravel(binarize(result.reshape(-1,1), 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict[predict<0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., ..., 1., 1., 1.], dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict[predict>0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The soil is wet\n"
     ]
    }
   ],
   "source": [
    "if predict[0]>0.5:\n",
    "    print(\"The soil is wet\")\n",
    "else:\n",
    "    print(\"The soil is dry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The soil is dry\n"
     ]
    }
   ],
   "source": [
    "if predict[0]<0.5:\n",
    "    print(\"The soil is wet\")\n",
    "else:\n",
    "    print(\"The soil is dry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def soleinoi_off_on():\n",
    "    if predict[1]>0.5:\n",
    "        \n",
    "        return 'off'\n",
    "    else:\n",
    "        return  'on'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "off\n"
     ]
    }
   ],
   "source": [
    "print(soleinoi_off_on())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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