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%%writefile ../../src/plots/plot_utils.py
# %load ../../src/plots/plot_utils.py
# %%writefile ../../src/plots/plot_utils.py
"""
Author: Jim Clauwaert
Created in the scope of my PhD
"""


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def HeatmapCVSinglePar(GS):
    
    if len(GS.param_grid) > 1:
        print("Does not accept more than 1 parameter")

    k = GS.n_splits_

    scores = np.array([])
    for fold in range(k):
        scores = np.append(scores,GS.cv_results_['split{}_test_score'.format(fold)])

    scores = scores.reshape(k,-1)

    for key in GS.param_grid:

        extent=[GS.param_grid[key].min(),GS.param_grid[key].max(),k+0.5,0.5]

        fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10, 4))
        fig.suptitle("{}".format(key),fontsize=14, 
                 fontweight='bold')
        ax1.set_title("Cross Validation") 
        ax1.set_yticks(np.arange(k+1))
        cax1 = ax1.imshow(scores, cmap='hot', vmin=0 , 
                          interpolation='nearest',extent=extent)
        fig.colorbar(cax1, ax=ax1)

        ax2.set_title("Cross Validation: Normalized")
        cax2 = ax2.imshow(sklearn.preprocessing.normalize(scores, axis=1), cmap='hot', vmin=0, 
                          interpolation='nearest',extent=extent)
        fig.colorbar(cax2, ax=ax2)

        ax1.set_xlabel('parameter value'), ax2.set_xlabel('parameter value')
        ax1.set_ylabel('k fold'), ax2.set_ylabel('k fold')
        ax1.set_xscale('log'), ax2.set_xscale('log')
        
        return fig, (ax1, ax2)
        
def HeatmapCVDoublePar(GS):
    
    if len(GS.param_grid) is not 2:
        print("Model has to evaluate two parameters")

    keyLengths = []
    keys = []
    for key in GS.param_grid:
        keys.append(key)
        keyLengths.append(len(GS.param_grid[key]))

    scores = np.flipud(GS.cv_results_["mean_test_score"].reshape(keyLengths))

    xMin, xMax = GS.param_grid[keys[0]].min(), GS.param_grid[keys[0]].max()
    yMin, yMax = GS.param_grid[keys[1]].min(), GS.param_grid[keys[1]].max()

    fig, ax = plt.subplots()
    ax.set_title("Model accuracy wrt. model parameters")
    cax = ax.imshow(scores, cmap='hot', interpolation='none', 
                    vmin=0, vmax=np.max(GS.cv_results_["mean_test_score"]),  
                    extent=[xMin,xMax,yMin,yMax], 
                    aspect="auto", 
              )
    fig.colorbar(cax)
    ax.set_xlabel(keys[0], fontweight='bold'), plt.xscale('log')
    ax.set_ylabel(keys[1], fontweight='bold'), plt.yscale('log')

    return fig, ax

def ScatterPvT(model, X, yTrue):
    
    yPred = model.predict(X)
    yMax = max(yTrue) if max(yTrue)>max(yPred) else max(yPred)
    
    fig, ax = plt.subplots( figsize=(6,4))
    ax.set_title("pred/true: K-fold")
    ax.plot([0,yMax],[0,yMax], ls="--", c=".3")
    ax.plot(yTrue,yPred, 'bo')
    ax.set_xlabel("True label")
    ax.set_ylabel("Predicted label")
    ax.text(0.05,0.9, 'score: '+ str(model.score(X,yTrue)),
        fontsize=10, transform=ax.transAxes)

    return fig, ax

def LearningCurveInSample(scores, k, step, dataLabel, scoring):
    
    titlesuffix = ""
    
    if dataLabel is not None:
        titlesuffix = " of " + dataLabel + " dataset"
    
    fig, (ax1,ax2) = plt.subplots(2,1, figsize=(10,8),sharex=True)
    ax1.set_title("Learning curve in sample score" + titlesuffix)
    for i in range(k):
        colors = ['bo','ro','yo','go','wo','mo','co','ko','bo','co']
        ax1.plot(range(len(scores[i,:])),scores[i,:], colors[i])
    ax1.set_xlabel("Step")
    ax1.set_ylabel("Score ("+scoring+")")
    meanScores=np.mean(scores,axis=0)
    stdScores=np.std(scores,axis=0)
    ax2.errorbar(range(len(meanScores)), meanScores[:], stdScores[:])
    ax2.set_xlabel("Step")
    ax2.set_ylabel("Score ("+scoring+")")
    ax1.set_xticklabels((ax1.get_xticks()*step)+5)
   
    return fig, ax1, ax2

def LearningCurveInSampleEnriched(scores, k, step, dataLabel, dataLabelEnriched, scoring):
    
    titlesuffix = ""
    titlesuffixE = ""    
    
    if dataLabel is not None:
        titlesuffix = " of " + dataLabel + " dataset"
    if dataLabelEnriched is not None:
        if type(dataLabelEnriched) is list:
            dataLabelsEnriched = ",".join(dataLabelEnriched )
            titlesuffixE = "\n(enriched with " + dataLabelsEnriched + " dataset)"
        else:
            titlesuffixE = "\n(enriched with " + dataLabelEnriched + " dataset)"
    
    
    fig, (ax1,ax2) = plt.subplots(2,1, figsize=(10,8),sharex=True)
    ax1.set_title("Learning curve in sample score" + titlesuffix + titlesuffixE )
    for i in range(k):
        colors = ['bo','ro','yo','go','wo','mo','co','ko','bo','co']
        ax1.plot(range(len(scores[i,:])),scores[i,:], colors[i])
    ax1.set_xlabel("Step")
    ax1.set_ylabel("Score ("+scoring+")")
    meanScores=np.mean(scores,axis=0)
    stdScores=np.std(scores,axis=0)
    ax2.errorbar(range(len(meanScores)), meanScores[:], stdScores[:])
    ax2.set_xlabel("Step")
    ax2.set_ylabel("Score ("+scoring+")")
    ax1.set_xticklabels((ax1.get_xticks()*step))

    
    return fig, ax1, ax2

def LearningCurveOutOfSample(scores, step, labels, dataLabel, scoring):
    
    titlesuffix = ""
    if dataLabel is not None:
        titlesuffix = "of " + dataLabel + " dataset"
        
    fig, ax = plt.subplots(figsize=(8,6))
    ax.set_title("Learning curve out of sample score")
    colors = ['bo','ro','yo','go','wo','mo','co','ko','bo','co']
    for j in range(len(scores)):
        if labels is not None:
            ax.plot(range(len(scores[j,:])),scores[j,:], colors[j], label=labels[j])
        else:
            print("no labels were given for out of sample datasets")
            ax.plot(range(len(scores[j,:])),scores[j,:], colors[j])
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
    ax.set_xlabel("Step")
    ax.set_ylabel("Score ("+scoring+")")
    ax.set_xticklabels((ax.get_xticks()*step)+5)
    
    return fig, ax

def LearningCurveOutOfSampleEnriched(scores, step, labels, dataLabel, dataLabelEnriched, scoring):
    
    titlesuffix = ""
    titlesuffixE = ""    
    if dataLabel is not None:
        titlesuffix = " of " + dataLabel + " dataset"
    if dataLabelEnriched is not None:
        if type(dataLabelEnriched) is list:
            dataLabelsEnriched = ",".join(dataLabelEnriched )
            titlesuffixE = "\n(enriched with " + dataLabelsEnriched + " dataset)"
        else:
            titlesuffixE = "\n(enriched with " + dataLabelEnriched + " dataset)"
    
    fig, ax = plt.subplots()
    ax.set_title("Learning curve out of sample score" + titlesuffix + titlesuffixE)
    colors = ['bo','ro','yo','go','wo','mo','co','ko','bo','co']
    for j in range(len(scores)):
        if labels is not None:
            ax.plot(range(len(scores[j,:])),scores[j,:], colors[j], label=labels[j])
        else:
            print("no labels were given for figures")
            ax.plot(range(len(scores[j,:])), scores[j,:], colors[j])
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
    ax.set_xlabel("Step")
    ax.set_ylabel("Score ("+scoring+")")
    ax.set_xticklabels((ax.get_xticks()*step))
    
    return fig, ax









    



Overwriting ../../src/plots/plot_utils.py