###########################################
# Suppress matplotlib user warnings
# Necessary for newer version of matplotlib
import warnings
warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
#
# Display inline matplotlib plots with IPython
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'inline')
###########################################

import matplotlib.pyplot as pl
import matplotlib.patches as mpatches
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import f1_score, accuracy_score
from sklearn.model_selection import learning_curve

def distribution(data, transformed = False):
    """
    Visualization code for displaying skewed distributions of features
    """
    
    # Create figure
    fig = pl.figure(figsize = (11,5));

    # Skewed feature plotting
    for i, feature in enumerate(['capital-gain','capital-loss']):
        ax = fig.add_subplot(1, 2, i+1)
        ax.hist(data[feature], bins = 25, color = '#00A0A0')
        ax.set_title("'%s' Feature Distribution"%(feature), fontsize = 14)
        ax.set_xlabel("Value")
        ax.set_ylabel("Number of Records")
        ax.set_ylim((0, 2000))
        ax.set_yticks([0, 500, 1000, 1500, 2000])
        ax.set_yticklabels([0, 500, 1000, 1500, ">2000"])

    # Plot aesthetics
    if transformed:
        fig.suptitle("Log-transformed Distributions of Continuous Census Data Features", \
            fontsize = 16, y = 1.03)
    else:
        fig.suptitle("Skewed Distributions of Continuous Census Data Features", \
            fontsize = 16, y = 1.03)

    fig.tight_layout()
    fig.show()


def evaluate(results, accuracy, f1):
    """
    Visualization code to display results of various learners.
    
    inputs:
      - learners: a list of supervised learners
      - stats: a list of dictionaries of the statistic results from 'train_predict()'
      - accuracy: The score for the naive predictor
      - f1: The score for the naive predictor
    """
  
    # Create figure
    fig, ax = pl.subplots(2, 3, figsize = (11,7))
    pl.tight_layout()
    # Constants
    bar_width = 0.3
    colors = ['#A00000','#00A0A0','#00A000']
    
    # Super loop to plot four panels of data
    for k, learner in enumerate(results.keys()):
        for j, metric in enumerate(['train_time', 'acc_train', 'f_train', 'pred_time', 'acc_test', 'f_test']):
            for i in np.arange(3):
                
                # Creative plot code
                ax[j//3, j%3].bar(i+k*bar_width, results[learner][i][metric], width = bar_width, color = colors[k])
                ax[j//3, j%3].set_xticks([0.45, 1.45, 2.45])
                ax[j//3, j%3].set_xticklabels(["1%", "10%", "100%"])
                ax[j//3, j%3].set_xlabel("Training Set Size")
                ax[j//3, j%3].set_xlim((-0.1, 3.0))
    
    # Add unique y-labels
    ax[0, 0].set_ylabel("Time (in seconds)")
    ax[0, 1].set_ylabel("Accuracy Score")
    ax[0, 2].set_ylabel("F-score")
    ax[1, 0].set_ylabel("Time (in seconds)")
    ax[1, 1].set_ylabel("Accuracy Score")
    ax[1, 2].set_ylabel("F-score")
    
    # Add titles
    ax[0, 0].set_title("Model Training")
    ax[0, 1].set_title("Accuracy Score on Training Subset")
    ax[0, 2].set_title("F-score on Training Subset")
    ax[1, 0].set_title("Model Predicting")
    ax[1, 1].set_title("Accuracy Score on Testing Set")
    ax[1, 2].set_title("F-score on Testing Set")
    
    # Add horizontal lines for naive predictors
    ax[0, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
    ax[1, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
    ax[0, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
    ax[1, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
    
    # Set y-limits for score panels
    ax[0, 1].set_ylim((0, 1))
    ax[0, 2].set_ylim((0, 1))
    ax[1, 1].set_ylim((0, 1))
    ax[1, 2].set_ylim((0, 1))

    # Create patches for the legend
    patches = []
    for i, learner in enumerate(results.keys()):
        patches.append(mpatches.Patch(color = colors[i], label = learner))
    pl.legend(handles = patches, bbox_to_anchor = (-.80, 2.53), \
               loc = 'upper center', borderaxespad = 0., ncol = 3, fontsize = 'x-large')
    
    # Aesthetics
    pl.suptitle("Performance Metrics for Three Supervised Learning Models", fontsize = 16, y = 1.10)
    pl.tight_layout()
    pl.show()
    

def feature_plot(importances, X_train, y_train):
    
    # Display the five most important features
    indices = np.argsort(importances)[::-1]
    columns = X_train.columns.values[indices[:5]]
    values = importances[indices][:5]

    # Creat the plot
    fig = pl.figure(figsize = (9,5))
    pl.title("Normalized Weights for First Five Most Predictive Features", fontsize = 16)
    pl.bar(np.arange(5), values, width = 0.6, align="center", color = '#00A000', \
          label = "Feature Weight")
    pl.bar(np.arange(5) - 0.3, np.cumsum(values), width = 0.2, align = "center", color = '#00A0A0', \
          label = "Cumulative Feature Weight")
    pl.xticks(np.arange(5), columns)
    pl.xlim((-0.5, 4.5))
    pl.ylabel("Weight", fontsize = 12)
    pl.xlabel("Feature", fontsize = 12)
    
    pl.legend(loc = 'upper center')
    pl.tight_layout()
    pl.show()  

def plot_learning_curve(plt, estimator, title, X, y, ylim=None, cv=None,
                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
    """
    Generate a simple plot of the test and training learning curve.

    Parameters
    ----------
    estimator : object type that implements the "fit" and "predict" methods
        An object of that type which is cloned for each validation.

    title : string
        Title for the chart.

    X : array-like, shape (n_samples, n_features)
        Training vector, where n_samples is the number of samples and
        n_features is the number of features.

    y : array-like, shape (n_samples) or (n_samples, n_features), optional
        Target relative to X for classification or regression;
        None for unsupervised learning.

    ylim : tuple, shape (ymin, ymax), optional
        Defines minimum and maximum yvalues plotted.

    cv : int, cross-validation generator or an iterable, optional
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:
          - None, to use the default 3-fold cross-validation,
          - integer, to specify the number of folds.
          - An object to be used as a cross-validation generator.
          - An iterable yielding train/test splits.

        For integer/None inputs, if ``y`` is binary or multiclass,
        :class:`StratifiedKFold` used. If the estimator is not a classifier
        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validators that can be used here.

    n_jobs : integer, optional
        Number of jobs to run in parallel (default 1).
    """
    fig = plt.figure(figsize = (9,5))
 
    plt.title(title)
    if ylim is not None:
        plt.ylim(*ylim)
    plt.xlabel("Training examples")
    plt.ylabel("Score")
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()

    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.1,
                     color="r")
    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                     test_scores_mean + test_scores_std, alpha=0.1, color="g")
    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
             label="Training score")
    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
             label="Cross-validation score")

    plt.legend(loc="best")
    return plt

