.EPA_output-6.ipynb

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    "## push Appmode to run the app!\n",
    "\n",
    "#### please email wonm@equinor.com for infomation or questions\n",
    "#### made by Ming Yi Wong, crude oil evaluation team\n"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "# Using plotly + cufflinks in offline mode\n",
    "import cufflinks\n",
    "cufflinks.go_offline(connected=True)\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "combi = pd.read_table('Historical_combi2.csv',delimiter =',')\n",
    "\n",
    "import datetime\n",
    "datetimes = [datetime.datetime.strptime(d, '%d/%m/%Y') for d in combi[\"Date\"]]\n",
    "\n",
    "\n",
    "df = pd.DataFrame(datetimes, columns=['date'])\n",
    "df.loc[:, 'date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\n",
    "combi = combi.assign(df=df['date'].values)\n",
    "\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "\n",
    "input1 = pd.read_table('inputdata.csv',delimiter=',')\n",
    "\n",
    "DB = input1[\"Dated Brent\"]\n",
    "Po = input1[\"Propane\"]\n",
    "Gasoline = input1[\"Gasoline\"]\n",
    "Jet = input1[\"Jet\"]\n",
    "Go = input1[\"Gasoil\"]\n",
    "FO1 = input1[\"FO 1%\"]\n",
    "ULSD = input1[\"ULSD\"]\n",
    "Na = input1[\"Naphtha\"]\n",
    "Bu = input1[\"Butane\"]\n",
    "FO35 = input1[\"FO 3.5%\"]\n",
    "VGO05 = input1[\"VGO 0.5%\"]\n",
    "FO05 = input1[\"FO 0.5%\"]\n",
    "VGO2 = input1[\"VGO 2%\"]\n",
    "Year = input1[\"Year\"]\n",
    "\n",
    "\n",
    "df = input1\n",
    "\n",
    "iplot({'data':[{'x': df['Year'],\n",
    "           'y': df[col],\n",
    "           'name': col} for col in df.columns[1:]],\n",
    "\n",
    "     'layout':\n",
    "         {'title':'Predicted brent and product prices'}\n",
    "}, show_link=False)\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "def esc(code):\n",
    "    return f'\\033[{code}m'\n",
    "\n",
    "def plotdiag(combi,model):\n",
    "    \n",
    "    model = model.replace(\"_\", \" \")\n",
    "    plt.rcParams['figure.dpi'] = 100\n",
    "    rma60 = combi[model].rolling(window=60).mean()\n",
    "    ema60 = combi[model].ewm(span=60, adjust=False).mean()\n",
    "    #plt.plot(combi[\"df\"],combi[model])\n",
    "    #plt.plot(combi[\"df\"],rma60)\n",
    "    #plt.plot(combi[\"df\"],ema60)\n",
    "    #plt.legend((\"Monthly\",\"rma\",\"ema\"))\n",
    "    #plt.show()\n",
    "    \n",
    "    time= combi[\"df\"]\n",
    "    means = [time,combi[model],rma60,ema60]\n",
    "    means_show = pd.concat(means, axis =1)\n",
    "    means_show.columns = ['time','Historic','rma60','ema60']\n",
    "    \n",
    "    df = means_show\n",
    "    \n",
    "    iplot({'data':[{'x': df['time'],\n",
    "           'y': df[col],\n",
    "           'name': col} for col in df.columns[1:]],\n",
    "\n",
    "     'layout':\n",
    "         {'title':'plotting different means'}\n",
    "    }, show_link=False)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    output = [combi['df'],rma60,ema60]\n",
    "    output_all = pd.concat(output, axis=1)\n",
    "    return output_all\n",
    "\n",
    "def regressionlinear(X,Y):\n",
    "    regr = linear_model.LinearRegression()\n",
    "    regr.fit(X, Y)\n",
    "    #print('Intercept: \\n', regr.intercept_)\n",
    "    #print('Coefficients: \\n', regr.coef_)\n",
    "\n",
    "    X = sm.add_constant(X.to_numpy())\n",
    "    model = sm.OLS(Y, X).fit()\n",
    "    predictions = model.predict(X) \n",
    " \n",
    "    print_model = model.summary()\n",
    "    #print(print_model)\n",
    "    \n",
    "    return regr.intercept_,regr.coef_\n",
    "\n",
    "\n",
    "\n",
    "def randomforest(x_train,y_train,x_test,y_test):\n",
    "    \n",
    "    plt.rcParams['figure.dpi'] = 100\n",
    "\n",
    "    regressor = RandomForestRegressor(n_estimators=200, max_depth=5)\n",
    "    clf=regressor.fit(x_train, y_train)\n",
    "\n",
    "    y_pred=regressor.predict(x_test)\n",
    "    y_pred=pd.DataFrame(y_pred)\n",
    "\n",
    "\n",
    "    plt_train=plt.scatter(x_train.iloc[:,0],y_train,   color='grey')\n",
    "    plt_test=plt.scatter(x_test.iloc[:,0],y_test,   color='green')\n",
    "    plt_pred=plt.scatter(x_test.iloc[:,0], y_pred,  color='black')\n",
    "\n",
    "    plt.xlabel(x_test.columns[0])\n",
    "    plt.ylabel(y_test.columns[0])\n",
    "    plt.legend((plt_train, plt_test,plt_pred),(\"train data\", \"test data\",\"prediction\"))\n",
    "    plt.show()\n",
    "\n",
    "    print(\"Mean squared error: %.2f\" % np.mean((regressor.predict(x_train) - y_train) ** 2))\n",
    "\n",
    "    import seaborn as sns\n",
    "    importances=regressor.feature_importances_\n",
    "\n",
    "    indices=list(x_train)\n",
    "    print(\"Feature ranking:\")\n",
    "    \n",
    "    for f in range(x_train.shape[1]):\n",
    "        print(\"Feature %s (%f)\" % (indices[f], importances[f]))\n",
    "\n",
    "    f, (ax1) = plt.subplots(1, 1, figsize=(6, 4), sharex=True)\n",
    "    sns.barplot(indices, importances, palette=\"BrBG\", ax=ax1)\n",
    "    ax1.set_ylabel(\"Importance\")\n",
    "\n",
    "    ax1.set_xticklabels(\n",
    "    ax1.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    "    );\n",
    "\n",
    "    return regressor.predict\n",
    "\n",
    "\n",
    "@interact(model=['Bonny_light', 'Urals_NWE','Urals_MED','Tapis','Maya_USGC','Maya_Europe','WTI','Dalia','Dubai','Azeri_BTC'\n",
    "                 ,'Arab_light','Arab_Heavy','Mars','ORL','Oseberg_blend'])\n",
    "\n",
    "      \n",
    "def NPV_Calc(model):\n",
    "    \n",
    "    \n",
    "    \n",
    "    if model == 'Bonny_light':\n",
    "             \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "                           \n",
    "        #Non linear model\n",
    "        nonlinear_BL = 0.0164924882990988*(DB) + 4.43302177278368e-5*np.power(DB,2) - 0.157317431833725\n",
    "        #print(\"Non.linear predicted price =[\", nonlinear_BL,\"]\")\n",
    "        \n",
    "        #Linear model\n",
    "        Y = combi[['Bonny light']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent']].iloc[K[0]:]\n",
    "        #print(X)\n",
    "        #print(Y)\n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        linear_BL=intercept_+ coef_[0]*DB \n",
    "        #print(\"Linear predicted price =\", linear_BL)\n",
    "        \n",
    "        #Random forest\n",
    "               \n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[:150,:]\n",
    "        test =All.iloc[-100:,:]\n",
    "        \n",
    "        x_train=train[\"Dated Brent\"].to_frame()\n",
    "        y_train=train[\"Bonny light\"]\n",
    "        \n",
    "        x_test=test[\"Dated Brent\"].to_frame()\n",
    "        y_test=test[\"Bonny light\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "\n",
    "        #invar = {'Dated Brent':[DB]}\n",
    "        invar_df = DB.to_frame()\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "       \n",
    "        table = [Year,np.round(linear_BL,2),np.round(nonlinear_BL,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)\n",
    "        \n",
    "                \n",
    "      \n",
    "        \n",
    "    elif model == 'Urals_NWE':\n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "                \n",
    "        #Non linear model\n",
    "        nonlinear_UralN = 0.221579893076645*(FO35) + 0.0169570894526404*(DB) - 0.0154492977171762 \n",
    "        - 0.0697233738254049*Go - 0.00856228790867718*Go*(FO35) - 0.00421761896551571*(FO35)**2\n",
    "                \n",
    "        #Linear model\n",
    "        Y = combi[['Urals NWE']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','FO 3.5%',\"Gasoil\"]].iloc[K[0]:]\n",
    "        X=X.fillna(0)               \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_UralN=intercept_+ coef_[0]*DB + coef_[1]*FO35 + coef_[2]*Go\n",
    "                \n",
    "        #Random forest\n",
    "            \n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-100:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\",\"FO 3.5%\",\"Gasoil\"]]\n",
    "        y_train=train[\"Urals NWE\"]\n",
    "        \n",
    "        x_test=test[[\"Dated Brent\",\"FO 3.5%\",\"Gasoil\"]]\n",
    "        y_test=test[\"Urals NWE\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "\n",
    "        invar_df = pd.concat([DB,FO35,Go],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_UralN,2),np.round(nonlinear_UralN,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)\n",
    "        \n",
    "    elif model == 'Tapis':    \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #Non linear model\n",
    "        nonlinear_Tapis = 0.569028548370371 + 0.158148187922098*Jet + 1.72847337855664e-6*np.power(DB,3)\n",
    "        + 0.000198531291135097*Jet*np.power(ULSD,2) - 0.000213781975514373*np.power(ULSD,3)\n",
    "        \n",
    "        #Linear model\n",
    "        Y = combi[['Tapis']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','Jet','ULSD']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_Tapis=intercept_+ coef_[0]*DB + coef_[1]*Jet + coef_[2]*ULSD\n",
    "        \n",
    "        #Random forest\n",
    "               \n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-100:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\",\"Jet\",\"ULSD\"]]\n",
    "        y_train=train[\"Tapis\"]\n",
    "        \n",
    "        x_test=test[[\"Dated Brent\",\"Jet\",\"ULSD\"]]\n",
    "        y_test=test[\"Tapis\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "       \n",
    "        invar_df = pd.concat([DB,Jet,ULSD],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_Tapis,2),np.round(nonlinear_Tapis,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)\n",
    "               \n",
    "        \n",
    "    elif model == 'WTI':\n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #Non linear model\n",
    "        nonlinear_WTI = -15.142388486653 - 2.06420352656404*Bu - 0.00877784990583735*DB*VGO05\n",
    "        - 0.000867998048676867*np.power(Bu,3) - 0.0803378779475392*np.power(Bu,2)\n",
    "        \n",
    "        #Linear model\n",
    "        Y = combi[['WTI']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','Butane','VGO 0.5%']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_WTI=intercept_+ coef_[0]*DB + coef_[1]*Bu + coef_[2]*VGO05\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-80:,:]\n",
    "        test =All.iloc[:80,:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\",\"Butane\",\"VGO 0.5%\"]]\n",
    "        y_train=train[\"WTI\"]\n",
    "        \n",
    "        x_test=test[[\"Dated Brent\",\"Butane\",\"VGO 0.5%\"]]\n",
    "        y_test=test[\"WTI\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "\n",
    "        invar_df = pd.concat([DB,Bu,VGO05],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_WTI,2),np.round(nonlinear_WTI,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        print('Non.Linear prediction for WTI is not reliable')\n",
    "        display(tableshow)       \n",
    "        \n",
    "    elif model == 'Maya_USGC':   \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #nonlinear model\n",
    "        nonlinear_MayaU =  0.687587246470687*FO35 + 0.00911247235417213*np.power(Jet,2) \n",
    "        + 0.0036123829112612*np.power(FO35,2) - 0.242991626831371*Jet\n",
    "\n",
    "        #Linear model\n",
    "        Y = combi[['Maya USGC']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','FO 3.5%','Jet']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_MayaU=intercept_+ coef_[0]*DB + coef_[1]*FO35 + coef_[2]*Jet\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-150:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\",\"FO 3.5%\",\"Jet\"]]\n",
    "        y_train=train[\"Maya USGC\"]\n",
    "        \n",
    "        x_test=test[[\"Dated Brent\",\"FO 3.5%\",\"Jet\"]]\n",
    "        y_test=test[\"Maya USGC\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "\n",
    "        invar_df = pd.concat([DB,FO35,Jet],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        table = [Year,np.round(linear_MayaU,2),np.round(nonlinear_MayaU,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)      \n",
    "       \n",
    "        \n",
    "    elif model == 'Dubai':\n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #nonlinear model\n",
    "        nonlinear_Dubai = 0.231614617140716*(FO35) + 0.0567664299088329*(DB) \n",
    "        + 0.000111685887466789*(DB)*np.power(FO35,2) - 1.13165773385408 \n",
    "        - 3.92742103073686e-6*np.power(DB,3) - 0.0101251631604449*np.power(FO35,2)\n",
    "        \n",
    "        #Linear model\n",
    "        Y = combi[['Dubai']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','FO 3.5%','FO 1%']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_Dubai=intercept_+ coef_[0]*DB + coef_[1]*FO35 + coef_[2]*FO1\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-100:,:]\n",
    "        test =All.iloc[:-100:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\",\"FO 3.5%\",\"FO 1%\"]]\n",
    "        y_train=train[\"Dubai\"]\n",
    "        \n",
    "        x_test=test[[\"Dated Brent\",\"FO 3.5%\",\"FO 1%\"]]\n",
    "        y_test=test[\"Dubai\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "      \n",
    "        invar_df = pd.concat([DB,FO35,FO1],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "  \n",
    "        table = [Year,np.round(linear_Dubai,2),np.round(nonlinear_Dubai,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)    \n",
    "\n",
    "    elif model == 'Azeri_BTC':\n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #Nonlinear model\n",
    "        nonlinear_BTC = 2 - 0.0186675105114445*DB*np.exp(-7.82878945629328e-9*Po*Na*np.power(DB,3))\n",
    "        #linear model\n",
    "        Y = combi[['Azeri BTC']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','Propane','Naphtha']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_BTC=intercept_+ coef_[0]*DB + coef_[1]*Po + coef_[2]*Na\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[:-50,:]\n",
    "        test =All.iloc[-100:,:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\",\"Propane\",\"Naphtha\"]]\n",
    "        y_train=train[\"Azeri BTC\"]\n",
    "        \n",
    "        x_test=test[[\"Dated Brent\",\"Propane\",\"Naphtha\"]]\n",
    "        y_test=test[\"Azeri BTC\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "        invar_df = pd.concat([DB,Po,Na],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_BTC,2),np.round(nonlinear_BTC,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)\n",
    "    \n",
    "    elif model == 'Arab_light':\n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        #Nonlinear model\n",
    "        nonlinear_ArabL = 7.89067436674442 + 0.0158412920210973*Jet*(VGO2) \n",
    "        + 0.00760596666502651*np.power(DB,2)- 0.534520760449475*(DB)- 3.23806029482173e-5*np.power(DB,3) \n",
    "        - 1.39671145737361e-9*(VGO2)*np.power(DB,4)\n",
    "        \n",
    "        #Linearmodel\n",
    "        Y = combi[['Arab light']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','Jet','VGO 2%','ULSD']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_ArabL=intercept_+ coef_[0]*DB + coef_[1]*Jet + coef_[2]*VGO2 + coef_[3]*ULSD\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-150:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\",\"Jet\",\"VGO 2%\",\"ULSD\"]]\n",
    "        y_train=train[\"Arab light\"]\n",
    "       \n",
    "        x_test=test[[\"Dated Brent\",\"Jet\",\"VGO 2%\",\"ULSD\"]]\n",
    "        y_test=test[\"Arab light\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "       \n",
    "        invar_df = pd.concat([DB,Jet,VGO2,ULSD],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_ArabL,2),np.round(nonlinear_ArabL,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        print('Non.Linear prediction for Arab light is not reliable')\n",
    "        display(tableshow)    \n",
    "\n",
    "    \n",
    "        \n",
    "    elif model == 'Arab_Heavy': \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        #Nonlinear model\n",
    "        \n",
    "        try: \n",
    "            k = 13/Jet   \n",
    "        \n",
    "        except ZeroDivisionError:\n",
    "        \n",
    "            k = 0\n",
    "        \n",
    "        nonlinear_ArabH = 0.635846243381231*(FO35) + 0.500212454466471*Jet + k \n",
    "        + -0.0025957970628738/np.exp(0.635846243381231*(FO35) + 0.500212454466471*Jet) \n",
    "        - 5.90911403987227 - 0.0271336502751073*Jet*(FO35) - 0.0254885412647423*np.power(Jet,2)\n",
    "        \n",
    "        #Linear model\n",
    "        Y = combi[['Arab Heavy']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent','FO 3.5%','Jet']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_ArabH=intercept_+ coef_[0]*DB + coef_[1]*FO35 + coef_[2]*Jet \n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-150:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        x_train=train[[\"FO 3.5%\",\"Jet\"]]\n",
    "        y_train=train[\"Arab Heavy\"]\n",
    "       \n",
    "        x_test=test[[\"FO 3.5%\",\"Jet\"]]\n",
    "        y_test=test[\"Arab Heavy\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "       \n",
    "        invar_df = pd.concat([FO35,Jet],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        table = [Year,np.round(linear_ArabH,2),np.round(nonlinear_ArabH,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        print('Non.Linear prediction for Arab heavy is not reliable')\n",
    "        display(tableshow) \n",
    "        \n",
    "    elif model == 'Maya_Europe': \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        #Nonlinear model\n",
    "        nonlinear_MayaE =  0.494070816929047*(FO35) + 0.00213078770640727*Go**2 - 4.30356291903192\n",
    "\n",
    "        #Linear model\n",
    "        Y = combi[['Maya Europe']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['FO 3.5%','Gasoil',\"FO 1%\"]].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_MayaE=intercept_+ coef_[0]*FO35 + coef_[1]*Go + coef_[2]*FO1\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[:,:]\n",
    "        test =All.iloc[-100:,:]\n",
    "        \n",
    "        x_train=train[[\"FO 3.5%\",\"Gasoil\",\"FO 1%\"]]\n",
    "        y_train=train[\"Maya Europe\"]\n",
    "       \n",
    "        x_test=test[[\"FO 3.5%\",\"Gasoil\",\"FO 1%\"]]\n",
    "        y_test=test[\"Maya Europe\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "        invar_df = pd.concat([FO35,Go,FO1],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "               \n",
    "        table = [Year,np.round(linear_MayaE,2),np.round(nonlinear_MayaE,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow) \n",
    "        \n",
    "    elif model == 'Mars': \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #Nonlinear model\n",
    "        nonlinear_Mars =  0.00533572984584189*Jet + 0.00232258205864726*Jet**2 - 5.0468498330526 \n",
    "        - 0.379625720730507*(FO35) - 0.000535344925798245*(FO35)**3 - 0.0324550409045776*(FO35)**2\n",
    "\n",
    "        #Linear model\n",
    "        Y = combi[['Mars']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['FO 3.5%','Jet']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_Mars=intercept_+ coef_[0]*FO35 + coef_[1]*Jet \n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-150:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        x_train=train[[\"FO 3.5%\",\"Jet\"]]\n",
    "        y_train=train[\"Mars\"]\n",
    "       \n",
    "        x_test=test[[\"FO 3.5%\",\"Jet\"]]\n",
    "        y_test=test[\"Mars\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "        invar_df = pd.concat([FO35,Jet],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_Mars,2),np.round(nonlinear_Mars,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)\n",
    "        \n",
    "    elif model == 'Urals_MED': \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #Nonlinear model\n",
    "        nonlinear_UralM =  0.00724612659575245*(FO1)**2 + 0.00153428845373314*(FO1)**3 - 1.24167501962635 \n",
    "        - 0.03181704251647*(FO35) - 0.210034973409967*(FO1) - 0.0295232993636577*(FO1)*(FO35) \n",
    "        - 0.00160888639770049*(FO35)*(FO1)**2\n",
    "        \n",
    "        #Linear model\n",
    "        Y = combi[['Urals MED']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['FO 3.5%','FO 1%']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_UralM=intercept_+ coef_[0]*FO35 + coef_[1]*FO1\n",
    "        \n",
    "        \n",
    "      \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-150:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        #x_train=train[[\"FO 3.5%\",\"FO 1%\"]]\n",
    "        x_train=train[[\"FO 3.5%\"]]\n",
    "        y_train=train[\"Urals MED\"]\n",
    "       \n",
    "        #x_test=test[[\"FO 3.5%\",\"FO 1%\"]]\n",
    "        x_test=test[[\"FO 3.5%\"]]\n",
    "        y_test=test[\"Urals MED\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "             \n",
    "        invar_df = pd.concat([FO35],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_UralM,2),np.round(nonlinear_UralM,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)   \n",
    "          \n",
    "        \n",
    "    elif model == 'ORL': \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        #Nonlinear model\n",
    "        nonlinear_ORL = 3.26973453707456e-6*Na*Bu*Po**2 - 0.314833608268645 - 0.0162685919989057*Na*Bu\n",
    "        \n",
    "        #Linear model\n",
    "        Y = combi[['ORL']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Naphtha','Butane','Propane']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_ORL=intercept_+ coef_[0]*Na + coef_[1]*Bu + coef_[2]*Po\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[:,:]\n",
    "        test =All.iloc[-100:,:]\n",
    "        \n",
    "        x_train=train[[\"Naphtha\",\"Butane\",\"Propane\"]]\n",
    "        y_train=train[\"ORL\"]\n",
    "       \n",
    "        x_test=test[[\"Naphtha\",\"Butane\",\"Propane\"]]\n",
    "        y_test=test[\"ORL\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "              \n",
    "        invar_df = pd.concat([Na,Bu,Po],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_ORL,2),np.round(nonlinear_ORL,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)\n",
    "\n",
    "    elif model == 'Oseberg_blend': \n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        \n",
    "        #Nonliner model\n",
    "        nonlinear_Ose = 0.011666686468756*(DB) + 3.67495911112595e-7*np.power(DB,3)\n",
    "        \n",
    "        #linear model\n",
    "        Y = combi[['Oseberg blend']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['Dated Brent']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_Ose=intercept_+ coef_[0]*DB\n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[:-70,:]\n",
    "        test =All.iloc[-100:,:]\n",
    "        \n",
    "        x_train=train[[\"Dated Brent\"]]\n",
    "        y_train=train[\"Oseberg blend\"]\n",
    "       \n",
    "        x_test=test[[\"Dated Brent\"]]\n",
    "        y_test=test[\"Oseberg blend\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "            \n",
    "        invar_df = pd.concat([DB],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_Ose,2),np.round(nonlinear_Ose,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        print('Non.Linear prediction for Ormen Lange is not reliable')\n",
    "        display(tableshow)\n",
    "        \n",
    "    elif model == 'Dalia':\n",
    "        \n",
    "        plotdiag(combi,model)\n",
    "        #Nonliner model\n",
    "        nonlinear_Dalia =  0.239431509820856*(FO1) + 0.00159358124012133*(DB)*(FO1)\n",
    "        + 0.000155571867417853*(DB)*(FO1)**2\n",
    "        \n",
    "        #linear model\n",
    "        Y = combi[['Dalia']].dropna()\n",
    "        K=list(Y.index.values)\n",
    "        K[0]\n",
    "        X = combi[['FO 1%','Dated Brent']].iloc[K[0]:]\n",
    "        X = X.fillna(0)       \n",
    "        intercept_, coef_ = regressionlinear(X,Y)\n",
    "        coef_ = coef_.reshape(-1)\n",
    "        linear_Dalia=intercept_+ coef_[0]*FO1 + coef_[1]*DB \n",
    "        \n",
    "        #Random forest\n",
    "        All = pd.concat([X,Y],axis=1,sort=False)\n",
    "        train = All.iloc[-150:,:]\n",
    "        test =All.iloc[:-100,:]\n",
    "        \n",
    "        x_train=train[[\"FO 1%\",\"Dated Brent\"]]\n",
    "        y_train=train[\"Dalia\"]\n",
    "       \n",
    "        x_test=test[[\"FO 1%\",\"Dated Brent\"]]\n",
    "        y_test=test[\"Dalia\"].to_frame()\n",
    "        F=randomforest(x_train,y_train,x_test,y_test)\n",
    "        \n",
    "               \n",
    "        invar_df = pd.concat([FO1, DB],axis = 1)\n",
    "        y_pred = F(invar_df)\n",
    "        y_pred=pd.Series(y_pred)\n",
    "        \n",
    "        \n",
    "        table = [Year,np.round(linear_Dalia,2),np.round(nonlinear_Dalia,2),np.round(y_pred,2)]\n",
    "        tableshow = pd.concat(table, axis =1)\n",
    "        tableshow.columns = ['Year','Linear','Nonlinear','Randomforest']\n",
    "        display(tableshow)\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        print('choose something from drop down menu')   \n",
    "\n",
    "    return\n",
    "\n",
    "\n",
    "\n"
   ]
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