In [1]:

    

import numpy as np
import scipy as sp 
import pandas as pd

import warnings
warnings.filterwarnings("ignore")


    

In [2]:

    

np.random.seed(71)
x = np.arange(10)
y = x

# Plot the most naive matplotlib plot. :-)
import matplotlib.pyplot as plt
plt.plot(x, y)
plt.show()


    

In [3]:

    

np.random.seed(71)
x = np.random.random(50)
y = np.random.random(50)

# Plot the most naive matplotlib line plot. :-)
import matplotlib.pyplot as plt
plt.scatter(x, y)
plt.show()


    

In [4]:

    

def plot_line(x, y, title='Line Plot', xlab='xlab', ylab='lab', 
              label='label', lw=2, xlim=None, ylim=None):
    """Line.
    
    Args:
      x: NumPy array, x vector.
      y: NumPy array, y vector.
      title: string, title.
      xlab: string, x-axis label.
      ylab: string, y-axis label.
      label: string, line label.
      lw: scalar, line width.
      xlim: list with [x_lower, x_upper]. Default to None.
      ylim: list with [y_lower, y_upper]. Default to None.
    """
    import matplotlib.pyplot as plt
    import seaborn as sns
    import matplotlib
    matplotlib.style.use('ggplot')
    %matplotlib inline

    plt.figure()
    plt.plot(x, y, color='darkorange', lw=lw, label=label)
    plt.title(title)
    plt.xlabel(xlab)
    plt.ylabel(ylab)
    plt.legend(loc='lower right')
    if xlim is not None:
        plt.xlim(xlim)
    if ylim is not None:
        plt.ylim(ylim)
    plt.show()


    

In [5]:

    

np.random.seed(71)
x = np.arange(5)
y = x

plot_line(x, y)


    

In [6]:

    

def plot_scatter(x, y, title='Scatter Plot', xlab='xlab', ylab='lab',  
                 color='b', marker='.', lw=2.5,
                 xlim=None, ylim=None):
    """Scatter plot.
    
    Args:
      x: NumPy array, x vector.
      y: NumPy array, y vector.
      title: string, title.
      xlab: string, x-axis label.
      ylab: string, y-axis label.
      color: string, point color. Default to 'b'. 
      marker: string, point marker. Default to '.'.
      lw: scalar, line width. Default to 2.5.
      xlim: list with [x_lower, x_upper]. Default to None.
      ylim: list with [y_lower, y_upper]. Default to None.
    """
    import matplotlib.pyplot as plt
    import seaborn as sns
    import matplotlib
    matplotlib.style.use('ggplot')
    %matplotlib inline

    plt.figure()
    plt.scatter(x, y, color=color, marker=marker, lw=lw)
    plt.title(title)
    plt.xlabel(xlab)
    plt.ylabel(ylab)
    plt.legend(loc='lower right')
    if xlim is not None:
        plt.xlim(xlim)
    if ylim is not None:
        plt.ylim(ylim)
    plt.show()


    

In [7]:

    

np.random.seed(71)
x = np.random.random(50)
y = np.random.random(50)

plot_scatter(x, y)


    

In [8]:

    

def plot_roc(y_true, prob_pred, title='ROC Curve', 
             xlab='False Positive (FP) Rate', ylab='True Positive (TP) Rate', lw=2):
    """Plot Receiver Operating Characteristics (ROC) Curve.

    Args:
      y_true: numpy array, labels in test set.
      prob_pred: numpy array, predicted probability for test set.
      title: string, title.
      xlab: string, x-axis label.
      ylab: string, y-axis label.
      lw: scalar, line width.
    """
    from sklearn.metrics import roc_curve, auc 
    import matplotlib.pyplot as plt
    import seaborn as sns
    import matplotlib
    matplotlib.style.use('ggplot')
    %matplotlib inline
    
    fpr, tpr, thresholds = roc_curve(y_true, prob_pred)
    roc_auc = auc(fpr, tpr)
    
    plt.figure()
    plt.plot(fpr, tpr, color='darkorange',
             lw=lw, label='ROC Curve (AUC = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.title(title)
    plt.xlabel(xlab)
    plt.ylabel(ylab)
    plt.legend(loc='lower right')
    plt.show()