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MQSensorsLib/Internal_design_documents/Experiments/.ipynb_checkpoints/MQ303_Regression-checkpoint.ipynb
miguel5612 9338da06e9 Linguistic fix
2019-08-15 11:05:19 -05:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n",
"Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n",
"Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\n",
"Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
"Requirement already satisfied: xlrd in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.0)\n",
"Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
"Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.21.2)\n",
"Requirement already satisfied: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n",
"Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n",
"Requirement already satisfied: numpy>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n",
"Requirement already satisfied: imblearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
"Requirement already satisfied: imbalanced-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from imblearn) (0.5.0)\n",
"Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n",
"Requirement already satisfied: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n",
"Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.2)\n",
"Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\n"
]
}
],
"source": [
"!pip install pandas\n",
"!pip install xlrd\n",
"!pip install sklearn\n",
"!pip install imblearn"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xlrd\n",
"book = xlrd.open_workbook(\"Datasheets info.xlsx\")\n",
"sheetMQ2 = book.sheet_by_name(\"MQ2 - Pololulu\")\n",
"sheetMQ3 = book.sheet_by_name(\"MQ3 - Sparkfun\")\n",
"sheetMQ4 = book.sheet_by_name(\"MQ4 - Sparkfun\")\n",
"sheetMQ5 = book.sheet_by_name(\"MQ5 - Sparkfun\")\n",
"sheetMQ6 = book.sheet_by_name(\"MQ6 - Sparkfun\")\n",
"sheetMQ7 = book.sheet_by_name(\"MQ7 - Sparkfun\")\n",
"sheetMQ8 = book.sheet_by_name(\"MQ8 - Sparkfun\")\n",
"sheetMQ9 = book.sheet_by_name(\"MQ9 - Haoyuelectronics\")\n",
"sheetMQ131 = book.sheet_by_name(\"MQ131- Sensorsportal\")\n",
"sheetMQ135 = book.sheet_by_name(\"MQ135 - HANWEI\")\n",
"sheetMQ303A = book.sheet_by_name(\"MQ303A - HANWEI\")\n",
"sheetMQ309A = book.sheet_by_name(\"MQ309A - HANWEI\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.01 \t \t \t \n",
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"0.08 \t \t \t \n",
"0.09 \t \t \t \n",
"0.1 \t 3000.0 \t \t 1000.0\n",
"0.2 \t \t 300.0 \t \n",
"0.3 \t 300.0 \t 100.0 \t \n",
"0.4 \t 100.0 \t \t 30.0\n",
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"0.6 \t \t \t 10.0\n",
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"source": [
"for row_index in range(1,20): #reading first columns\n",
" RsR0, Iso_butano, Hidrogeno , Alcohol = sheetMQ303A.row_values(row_index, start_colx=0, end_colx=4)\n",
" print(RsR0, \"\t\", Iso_butano, \"\t\", Hidrogeno, \"\t\", Alcohol)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"x_MQ303A = sheetMQ303A.col_values(0)[2:]\n",
"MQ303A_Iso_butano = sheetMQ303A.col_values(1)[2:]\n",
"MQ303A_Hidrogeno = sheetMQ303A.col_values(2)[2:]\n",
"MQ303A_Alcohol = sheetMQ303A.col_values(3)[2:]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def zero_to_nan(values):\n",
" \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n",
" return [float('nan') if x==0 else x for x in values]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"MQ303A_Iso_butano =zero_to_nan(MQ303A_Iso_butano)\n",
"MQ303A_Hidrogeno =zero_to_nan(MQ303A_Hidrogeno)\n",
"MQ303A_Alcohol =zero_to_nan(MQ303A_Alcohol)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.datasets import load_iris\n",
"#from sklearn.cross_validation import train_test_split\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import datasets\n",
"from sklearn import linear_model\n",
"\n",
"dataIso_Butano = {'RsRo': x_MQ303A, 'Iso_Butano': MQ303A_Iso_butano}\n",
"dataHidrogeno = {'RsRo': x_MQ303A, 'Hidrogeno': MQ303A_Hidrogeno}\n",
"dataAlcohol = {'RsRo': x_MQ303A, 'Alcohol': MQ303A_Alcohol}\n",
"\n",
"dfMQ303A_Iso_Butano = pd.DataFrame(dataIso_Butano)\n",
"dfMQ303A_Hidrogeno = pd.DataFrame(dataHidrogeno)\n",
"dfMQ303A_Alcohol = pd.DataFrame(dataAlcohol)\n",
"\n",
"dfMQ303A_Iso_Butano['Iso_Butano'] = pd.to_numeric(dfMQ303A_Iso_Butano['Iso_Butano'])\n",
"dfMQ303A_Hidrogeno['Hidrogeno'] = pd.to_numeric(dfMQ303A_Hidrogeno['Hidrogeno'])\n",
"dfMQ303A_Alcohol['Alchol'] = pd.to_numeric(dfMQ303A_Alcohol['Alcohol'])\n",
"\n",
"dfMQ303A_Iso_Butano['Iso_Butano'] = dfMQ303A_Iso_Butano['Iso_Butano'].replace('',None, regex=True)\n",
"dfMQ303A_Hidrogeno['Hidrogeno'] = dfMQ303A_Hidrogeno['Hidrogeno'].replace('',None, regex=True)\n",
"dfMQ303A_Alcohol['Alchol'] = dfMQ303A_Alcohol['Alchol'].replace('',None, regex=True)\n",
"\n",
"#Global X_Predict variable\n",
"X_Predict = dfMQ303A_Iso_Butano.RsRo.apply(lambda x: [x]).tolist()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#Model and train Iso-Butano\n",
"dataset2TrainIso_Butano = dfMQ303A_Iso_Butano.copy()\n",
"dataset2TrainIso_Butano.dropna(inplace=True)\n",
"X_trainIso_Butano = dataset2TrainIso_Butano.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainIso_Butano = dataset2TrainIso_Butano['Iso_Butano'].tolist()\n",
"model = linear_model.Lasso(alpha=0.1)\n",
"model.fit(X_trainIso_Butano, y_trainIso_Butano)\n",
"#Predict\n",
"Iso_Butano_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
"MQ303A_Iso_Butano = Iso_Butano_Predicted"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#Model and train Hidrogeno\n",
"dataset2TrainHidrogeno = dfMQ303A_Hidrogeno.copy()\n",
"dataset2TrainHidrogeno.dropna(inplace=True)\n",
"X_trainHidrogeno = dataset2TrainHidrogeno.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainHidrogeno = dataset2TrainHidrogeno['Hidrogeno'].tolist()\n",
"model = linear_model.Lasso(alpha=0.1)\n",
"model.fit(X_trainHidrogeno, y_trainHidrogeno)\n",
"#Predict\n",
"Hidrogeno_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
"MQ303A_Hidrogeno = Hidrogeno_Predicted"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"#Model and train Alchol\n",
"dataset2TrainAlchol = dfMQ303A_Alcohol.copy()\n",
"dataset2TrainAlchol.dropna(inplace=True)\n",
"X_trainAlchol = dataset2TrainAlchol.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainAlchol = dataset2TrainAlchol['Alchol'].tolist()\n",
"model = linear_model.Lasso(alpha=0.1)\n",
"model.fit(X_trainAlchol, y_trainAlchol)\n",
"#Predict\n",
"Alchol_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
"MQ303A_Alchol = Alchol_Predicted\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
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"text/plain": [
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},
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],
"source": [
"%config InlineBackend.figure_formats = ['svg']\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.lines as mlines\n",
"import matplotlib.transforms as mtransforms\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"fig.set_size_inches(9, 5.5, forward=True)\n",
"fig.set_dpi(200)\n",
"\n",
"# only these two lines are calibration curves\n",
"plt.plot(MQ303A_Iso_Butano, x_MQ303A, marker='o', linewidth=1, label='Iso-Butano')\n",
"plt.plot(MQ303A_Hidrogeno, x_MQ303A, marker='o', linewidth=1, label='Hidrogeno')\n",
"plt.plot(MQ303A_Alchol, x_MQ303A, marker='o', linewidth=1, label='Alcohol')\n",
"\n",
"\n",
"\n",
"# reference line, legends, and axis labels\n",
"#line = mlines.Line2D([0, 1], [0, 1], color='black')\n",
"#transform = ax.transAxes\n",
"#line.set_transform(transform)\n",
"#ax.add_line(line)\n",
"plt.yscale('log')\n",
"plt.xscale('log')\n",
"plt.legend()\n",
"\n",
"plt.grid(b=True, which='minor', color='lightgrey', linestyle='--')\n",
"\n",
"fig.suptitle('Calibration plot for MQ-303A data')\n",
"ax.set_xlabel('PPM Concentration')\n",
"ax.set_ylabel('Rs/Ro')\n",
"\n",
"\n",
"#Save image\n",
"plt.savefig('MQ303A.svg', format = 'svg', dpi = 1200)\n",
"plt.savefig('MQ303A.png')\n",
"plt.savefig('MQ303A.eps', format = 'eps', dpi = 1200)"
]
},
{
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"execution_count": null,
"metadata": {},
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