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Example of the Workflow

This is an example of main.py in the ionic_liquids folder. This notebook is based upon the functions in utils.py and methods/methods.py. I have written this code in such a way that the function will come first and the calling of the function comes right after it. I will first have to import the libraries that are necessary to run this program, including train_test_split that allows for splitting datasets into training sets and test sets necessary to run machine learning.





In [1]:



    


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from rdkit import Chem
from rdkit.Chem import AllChem, Descriptors
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator as Calculator
import pandas as pd
import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.svm import SVR
from sklearn.neural_network import MLPRegressor
from numpy import linalg as LA

For this example, I will utilize the following filename, machine learning model, and directory name to save the model.





In [2]:



    


FILENAME = 'inputdata.xlsx'
MODEL = 'mlp_regressor'
DIRNAME = 'my_test'

The following step prepares the data to be read in the machine_learning methods. First, we need to get the data into a readable form and parse, if necessary. In our case, we need to parse the values and errors in the last column of the FILENAME.





In [3]:



    


def read_data(filename):
    """
    Reads data in from given file to Pandas DataFrame

    Inputs
    -------
    filename : string of path to file

    Returns
    ------
    df : Pandas DataFrame

    """
    cols = filename.split('.')
    name = cols[0]
    filetype = cols[1]
    if (filetype == 'csv'):
        df = pd.read_csv(filename)
    elif (filetype in ['xls', 'xlsx']):
        df = pd.read_excel(filename)
    else:
        raise ValueError('Filetype not supported')

    # clean the data if necessary
    df['EC_value'], df['EC_error'] = zip(*df['ELE_COD'].map(lambda x: x.split('±')))
    y_error = np.copy(df['EC_error'])
    df = df.drop('EC_error', 1)
    df = df.drop('ELE_COD', 1)

    return df, y_error

df, y_error = read_data(FILENAME)

We will send our data to the molecular descriptor function that will create the molecular descriptors for us, resulting in the X matrix and y vector. Specifically, the X matrix will hold all of our inputs for the machine learning whereas y vector will be the actual electronic conductivity values.





In [4]:



    


def molecular_descriptors(data):
    """
    Use RDKit to prepare the molecular descriptor

    Inputs
    ------
    data: dataframe, cleaned csv data

    Returns
    ------
    prenorm_X: dataframe, normalized input features
    Y: dataframe, experimental electrical conductivity

    """
    
    n = data.shape[0]
    # Choose which molecular descriptor we want
    list_of_descriptors = ['NumHeteroatoms', 'ExactMolWt',
        'NOCount', 'NumHDonors',
        'RingCount', 'NumAromaticRings', 
        'NumSaturatedRings', 'NumAliphaticRings']
    # Get the molecular descriptors and their dimension
    calc = Calculator(list_of_descriptors)
    D = len(list_of_descriptors)
    d = len(list_of_descriptors)*2 + 4
    #Setting up the X and Y matrices
    X = np.zeros((n, d))
    Y = data['EC_value']
    X[:, -3] = data['T']
    X[:, -2] = data['P']
    X[:, -1] = data['MOLFRC_A']
    for i in range(n):
        A = Chem.MolFromSmiles(data['A'][i])
        B = Chem.MolFromSmiles(data['B'][i])
        X[i][:D]    = calc.CalcDescriptors(A)
        X[i][D:2*D] = calc.CalcDescriptors(B)

    prenorm_X = pd.DataFrame(X,columns=['NUM', 'NumHeteroatoms_A', 
        'MolWt_A', 'NOCount_A','NumHDonors_A', 
        'RingCount_A', 'NumAromaticRings_A', 
        'NumSaturatedRings_A',
        'NumAliphaticRings_A', 
        'NumHeteroatoms_B', 'MolWt_B', 
        'NOCount_B', 'NumHDonors_B',
        'RingCount_B', 'NumAromaticRings_B', 
        'NumSaturatedRings_B', 
        'NumAliphaticRings_B',
        'T', 'P', 'MOLFRC_A'])

    prenorm_X = prenorm_X.drop('NumAliphaticRings_A', 1)
    prenorm_X = prenorm_X.drop('NumAliphaticRings_B', 1)

    return prenorm_X,Y

X, y = molecular_descriptors(df)

We can prepare our testing and training data set for the machine learning calling using train_test_split, a function called from sklearn module of python.





In [5]:



    


Y = np.empty((y.shape[0],2))
Y[:,0] = y.ravel()
Y[:,1] = y_error.ravel()

X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.10)

y_train = Y_train[:,0]
y_test = Y_test[:,0]
e_train = Y_train[:,1]
e_test = Y_test[:,1]

Followingly, the program will normalize the testing data using the training data set. This will also provide us with the mean value and standard deviation of X.





In [6]:



    


def normalization(data,means=None,stdevs=None):
    """
    Normalizes the data using the means and standard
    deviations given, calculating them otherwise.
    Returns the means and standard deviations of columns.

    Inputs
    ------
    data : Pandas DataFrame
    means : optional numpy argument of column means
    stdevs : optional numpy argument of column st. devs

    Returns
    ------
    normed : the normalized DataFrame
    means : the numpy row vector of column means
    stdevs : the numpy row vector of column st. devs

    """
    cols = data.columns
    data = data.values

    if (means is None) or (stdevs is None):
        means = np.mean(data, axis=0)
        stdevs = np.std(data, axis=0, ddof=1)
    else:
        means = np.array(means)
        stdevs = np.array(stdevs)

    # handle special case of one row
    if (len(data.shape) == 1) or (data.shape[0] == 1):
        for i in range(len(data)):
            data[i] = (data[i] - means[i]) / stdevs[i]
    else: 
        for i in range(data.shape[1]):
            data[:,i] = (data[:,i] - means[i]*np.ones(data.shape[0])) / stdevs[i]

    normed = pd.DataFrame(data, columns=cols)

    return normed, means, stdevs

X_train, X_mean, X_std = normalization(X_train)
X_test, trash, trash = normalization(X_test,means=X_mean,stdevs=X_std)

We coded three models into our program: MLP_regressor, LASSO, and SVR. Each of these models are well documented in sklearn, a library in python. In the actual program, you can use all three models, but for the purpose of this example, we chose mlp_regressor. The ValueError will only raise if you do not use one of the three models. A good example is if you were to change the MODEL used to 'MLP_classifier'.





In [7]:



    


def do_svr(X,Y):
    Y.ravel()
    """
    Call the Support Vector Regressor, 
    Fit the weight on the training set
    
    Input
    ------
    X: dataframe, n*m, n is number of data points, 
        m is number of features
    y: experimental electrical conductivity
    
    Returns
    ------
    svr: objective, the regressor objective 
    """
    
    svr = SVR(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001,
        C=1.0, epsilon=0.01, shrinking=True, cache_size=200, verbose=False, max_iter=-1)
    grid_search = GridSearchCV(svr, cv=5, param_grid={"C": [1e0, 1e1, 1e2, 1e3],"gamma": np.logspace(-2, 2, 5)})
    grid_search.fit(X,Y)
    
    svr.alpha_ = grid_search.best_params_['alpha']
    svr.fit(X,Y)
    return svr

def do_MLP_regressor(X,Y):
    Y.ravel()
    """
    Call the MLP Regressor, 
    Fit the weight on the training set
    
    Input
    ------
    X: dataframe, n*m, n is number of data points, 
        m is number of features
    y: experimental electrical conductivity
    
    Returns
    ------
    mlp_regr : the MLP object with the best parameters
    """    
    alphas = np.array([0.1,0.01,0.001,0.0001])
    mlp_regr = MLPRegressor(hidden_layer_sizes=(100,), activation='tanh',
        solver='sgd', alpha=0.0001, max_iter=5000, random_state=None,learning_rate_init=0.01)
    grid_search = GridSearchCV(mlp_regr, param_grid=dict(alpha=alphas))
    grid_search.fit(X,Y)
    
    #print(grid_search.best_params_)
    mlp_regr.alpha_ = grid_search.best_params_['alpha']
    mlp_regr.fit(X,Y)

    return mlp_regr

def do_lasso(X,Y): 
    Y.ravel()
    """
    Runs a lasso grid search on the input data
    
    Inputs
    ------
    X: dataframe, n*m, n is number of data points, 
        m is number of features
    y: experimental electrical conductivity
    
    Returns
    ------
    lasso : sklearn object with the model information 
    """
    
    alphas = np.array([0.1,0.01,0.001,0.0001])
    lasso = Lasso(alpha=0.001, fit_intercept=True, normalize=False, precompute=False,
        copy_X=True, max_iter=10000, tol=0.001, positive=False, random_state=None, selection='cyclic')
    gs = GridSearchCV(lasso, param_grid=dict(alpha=alphas))
    gs.fit(X,Y)
    
    lasso.alpha_ = gs.best_params_['alpha']

    lasso.fit(X,Y)
        
    return lasso

The code below are calling to the functions above and is the machine learning portion of the package.





In [8]:



    


if (MODEL.lower() == 'mlp_regressor'):
    obj = do_MLP_regressor(X_train, y_train.ravel())
elif (MODEL.lower() == 'lasso'):
    obj = do_lasso(X_train, y_train.ravel())
elif (MODEL.lower() == 'svr'):
    obj = do_svr(X_train, y_train.ravel())
else:
    raise ValueError("Model not supported")

Lastly, the experimental values will be scatter plotted against the predicted values. We will use the parity_plot to do so. plt.show() function will just allow the plot to show up.





In [9]:



    


FIG_SIZE = (4,4)

def parity_plot(Y_act, Y_pred):
    """
    Creates a parity plot

    Input
    -----
    y_pred : predicted values from the model (Lasso, SVR, or MLP)
    y_act : 'true' (actual) values

    Output
    ------
    fig : matplotlib figure

    """

    fig = plt.figure(figsize=FIG_SIZE)
    plt.scatter(list(Y_act), list(Y_pred))
    plt.plot([Y_act.min(), Y_act.max()], [Y_act.min(), Y_act.max()],
             lw=4, color='r')
    plt.xlabel('Experimental')
    plt.ylabel('Predicted')

    return fig

my_plot = parity_plot(y_train, obj.predict(X_train))
plt.show(my_plot)

The error_values function calculates and provides the prediction values to create the plot of prediction values versus the experimental values.





In [22]:



    


def error_values(X_train,X_test,Y_train,Y_test):
    """
    Creates the two predicted values

    Input
    -----
    X_train : numpy array, the 10% of the training set data values
    X_test : numpy array, the molecular descriptors for the testing set data values 

    Y_train: numpy array, the 10% of the training set of electronic conductivity values
    Y_test: numpy array, 'true' (actual) electronic conductivity values
    
    Output
    ------
    yh : the prediction output for training data set
    yh2 : the prediction output for the testing data set 
    """
    #setting up parameters and variables for plotting 
    n_train = X_train.shape[0]
    n_test = X_test.shape[0]
    d = X_train.shape[1]
    hdnode = 100
    w1 = np.random.normal(0,0.001,d*hdnode).reshape((d,hdnode))
    d1 = np.zeros((d,hdnode))
    w2 = np.random.normal(0,0.001,hdnode).reshape((hdnode,1))
    d2 = np.zeros(hdnode)
    h  = np.zeros(hdnode)
    mb = 100 #minibatch size
    m = int(n_train/mb)
    batch = np.arange(m) 
    lr = 0.00020
    EP = 20000 #needed for initializing 
    ep = 0
    y = np.zeros((mb,1))
    yh = np.zeros((n_train,1))
    yh2 = np.zeros((n_test,1))
    L_train= np.zeros(EP+1)
    L_test = np.zeros(EP+1)
    Y_train = Y_train.reshape(len(Y_train),1)
        
    #activation function for the hidden layer is tanh
    def g(A):
        return (np.tanh(A))

    def gd(A):
        return (1-np.square(np.tanh(A)))
        
    #setting up how long the epoch will run
    EP = 20
    ep = 0
    while ep < EP:
        ep += 1
        yh = g(X_train.dot(w1)).dot(w2)
        yh2 = g(X_test.dot(w1)).dot(w2)
        print(yh.dtype,yh2.dtype,Y_train.dtype,Y_test.dtype)
        L_train[ep] = LA.norm(yh-Y_train.reshape(len(Y_train),1))/n_train
        L_test[ep]  = LA.norm(yh2-Y_test.reshape(len(Y_test),1))/n_test
        
        np.random.shuffle(batch)
        for i in range(m):
            st = batch[i]*mb
            ed = (batch[i]+1)*mb
            h  = g(X_train[st:ed].dot(w1))
            y = h.dot(w2)
            d2 = h.T.dot(Y_train[st:ed]-y)
            print (d2.shape)
            print(h.shape, h.dtype, X_train.shape, X_train.dtype, y.shape, y.dtype)
            print((X_train[st:ed]).shape,(Y_train[st:ed]-y).shape)
            d1 = X_train[st:ed].T.dot(np.multiply((Y_train[st:ed]-y).dot(w2.T),gd(X_train[st:ed].dot(w1))))
            w2 += lr*d2
            w1 += lr*d1
    return yh, yh2

yh, yh2 = error_values(np.copy(X_train),np.copy(X_test),np.copy(y_train),np.copy(y_test))

#Creates a plot for the training data set predicted and experimental values 
plt.figure(figsize=(10,8))
plt.subplot(1,2,1)
plt.scatter(y_train,yh,s=0.5,color='blue')
plt.title('Prediction on training data')
plt.plot(np.linspace(0,12,1000),np.linspace(0,12,1000),color='black')
plt.xlim((0,12))
plt.ylim((0,12))
plt.xlabel("Experiment($S*m^2/mol$)")
plt.ylabel("Prediction($S*m^2/mol$)")

#Creates a plot for the testing data set predicted and experimental values 
plt.subplot(1,2,2)
plt.scatter(y_test,yh2,s=2,color='blue')
plt.title('Prediction on test data')
plt.xlim((0,12))
plt.ylim((0,12))
plt.xlabel("Experiment($S*m^2/mol$)")
plt.ylabel("Prediction($S*m^2/mol$)")
plt.plot(np.linspace(0,12,1000),np.linspace(0,12,1000),color='black')
plt.show()



    


















    






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The following input will provides a plot that will compare the experimental and predicted data to the experimental error provided by the ILThermo database. The x and y axis limits are based upon the percent that you pick in the test_training_split function.





In [13]:



    


fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)

#Set up the training data set plot
result = pd.DataFrame(columns=['Experiment','Prediction','error'])
result.Experiment = y_train
result.Prediction = yh
result.error = e_train
result = result.sort_values(['Experiment','Prediction'],ascending=[1,1])

size=0.2
ax1.set_xlim((0,2300))
ax1.set_ylim((-1,13))
ax1.scatter(np.arange(X_train.shape[0]),result.Experiment,color="blue",s=size,label='Experiment')
ax1.scatter(np.arange(X_train.shape[0]),result.Prediction,color="red",s=size,label='Prediction')
ax1.scatter(np.arange(X_train.shape[0]),result.Experiment+result.error,color="green",s=size,label='Experimental Error')
ax1.scatter(np.arange(X_train.shape[0]),result.Experiment-result.error,color="green",s=size)
ax1.set_title('Prediction on Training data')
ax1.legend(loc='upper left')

#setting up the test data set plot 
result = pd.DataFrame(columns=['Experiment','Prediction','error'])
result.Experiment = y_test
result.Prediction = yh2
result.error = e_test
result = result.sort_values(['Experiment','Prediction'],ascending=[1,1])

size=2
ax2.set_xlim((0,260))
ax2.set_ylim((-1,13))
ax2.scatter(np.arange(X_test.shape[0]),result.Experiment,color="blue",s=size,label='Experiment')
ax2.scatter(np.arange(X_test.shape[0]),result.Prediction,color="red",s=size,label='Prediction')
ax2.scatter(np.arange(X_test.shape[0]),result.Experiment+result.error,color="green",s=size,label='Experimental Error')
ax2.scatter(np.arange(X_test.shape[0]),result.Experiment-result.error,color="green",s=size)
ax2.set_title('Prediction on test data')
ax2.legend(loc='upper left')

ax.set_xlabel('Data points')
ax.set_ylabel('Conductivity($S*m^2/mol$)')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
fig.tight_layout()
plt.show()

I hope you enjoy our package and use it to fit your needs!

Content source: joekasp/ionic_liquids

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