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%matplotlib inline

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

matplotlib.style.use('ggplot')
pylab.rcParams['figure.figsize'] = 1, 10  # that's default image size for this interactive session


    

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working_dir = "../resources/results/results_supervised_sensem/"

experiments = {}

for lemma_dir in (d for d in os.listdir(working_dir) if not (d.endswith(".yaml") or d == ".RData" or d == ".Rhistory")):
    experiments[lemma_dir] = {}
    
    for experiment_dir in os.listdir(os.path.join(working_dir, lemma_dir)):
        accuracy = np.loadtxt(os.path.join(working_dir, lemma_dir, experiment_dir, "accuracy"))
        mcp = np.loadtxt(os.path.join(working_dir, lemma_dir, experiment_dir, "most_common_precision"))
        lcr = np.loadtxt(os.path.join(working_dir, lemma_dir, experiment_dir, "less_common_recall"))

        experiments[lemma_dir][experiment_dir] = pd.DataFrame({
                'accuracy': accuracy,
                'mcp': mcp,
                'lcr': lcr
            })


    

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accuracy_sensem = pd.read_csv("scripts/test_accuracy.csv", index_col=0)
accuracy_semeval = pd.read_csv("scripts/test_accuracy_semeval.csv", index_col=0)
accuracy_semeval_verbs_only = pd.read_csv("scripts/test_accuracy_semeval_verbs_only.csv", index_col=0)


    

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accuracy_sensem.boxplot(return_type='axes')


    

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accuracy_semeval.boxplot(return_type='axes')


    

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accuracy_semeval_verbs_only.boxplot(return_type='axes')


    

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