plot_confidences_.py.save

import numpy as np
import scipy.stats as ss
import pandas as pd
import matplotlib.pyplot as plt
import argparse
import logging
import itertools
import operator
from pdb import set_trace as st

logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
                    level=logging.INFO)


def feature_spectrum(human_ref, machines):
    return {f: 20.0 * np.log10((1 + machines[f]) / (1 + human_ref[f]))
                                                         for f in machines}


# __main__()
parser = argparse.ArgumentParser()
parser.add_argument("--aspect", help="""Aspect ratio. aspect>1 reduces horizontal scale
                                        and aspect<1 reduces vectical scale.""",
                                        default=1.0, type=float)
parser.add_argument("--ndocs", help="""Documents per annotator to sample.""", default=25,
                                        type=int)
parser.add_argument("--feat", help="""If a specific feature is needed to be ploted.""",
                                        default=None)
parser.add_argument("--sp_bx", help="""If you need feature spectrum pass 'sp', and if
                                    you need boxplots pass 'bx'.""",
                                        default='bx')
#parser.add_argument("--outdir", help="Output directory.", default=None)
args = parser.parse_args()

linewidth=2
aspect=args.aspect
docs_per_annotator=args.ndocs
vertical=True
in_file="all_summ_tables_samp_%d_dev_.csv" % docs_per_annotator
directory=""  #"/home/iarroyof/Documentos/summ_feats/RESULTS/"

non_feats=['Unnamed: 0', 'index', 'system', 'system.1']

if args.feat.startswith("inf"):
    may_feats=['IN_AVG', 'IN_MED','IN_MIN','IN_NNP','JJ_AVG',
            'named_entities','NNP','NNP_AVG','NNP_IN','NNP_MAX',
            'NNP_MED','NNP_MIN','NNP_NN','NNP_NNP','NNP_POS',
            'NNPS','NNPS_AVG','NNP_VBZ','NNS_MED','NNS_PERIOD',
            'no_corefs','no_tokens','sent_sentim_3','tok_sentim_2',
            'TotalRST','VBD','VBD_AVG','VBD_MAX','VBD_MED',
            'VBD_NNP','CC','CD_MAX','CD_MED']
    only_feat=['DATE', 'IN', 'NNP_NNP', 'no_openie', 
            'tok_sentim_1', 'tok_sentim_2', 'tok_sentim_3',
            'sent_sentim_1', 'sent_sentim_2', 'sent_sentim_3'] + may_feats
elif not args.feat == "all":
    only_feat=[args.feat]

sys_tag="system"
sources=["_movies", "_experiments", "_abstracts", "_baseline", "_human_tab"]
regexs={#sources[0]: sources[0]+"_\d{1,8}_(\d{3}).txt",
        sources[0]: sources[0]+"_\d{1,8}_(1[0-1][0-5]|09[5-9]).txt", # matches number in [95,115]
        sources[1]: sources[1]+"_d\d{5}t\.(\w+)",
        #sources[2]: sources[2]+"_\d{4}_(\d{3})",
        sources[2]: sources[2]+"_\d{4}_(1[0-1][0-5]|09[5-9])", # matches number in [95,115]
        sources[3]: sources[3]+"_d\d{5}t\.(\w+)",
        sources[4]: sources[4]+"_D\d{5}.M.100.T.([A-H])"
        }
final_src={sources[0]: "Humans_movi_",
           sources[1]: "Machines_soa_",
           sources[2]: "Humans_abstr_",
           sources[3]: "Machines_base_",
           sources[4]: "Humans_duc_"
               }

table_all = pd.read_csv(directory + in_file, sep=',')
src_feat = {}
for s_ in regexs:
    annotators = [s for s in table_all[sys_tag].str.extract(regexs[s_])[0].unique()
                                                                if not pd.isnull(s)]
    for a in annotators:
        src_feat[final_src[s_] + a] = table_all[table_all[sys_tag].str.contains(
                                                s_ + ".*" + a + "(.txt|$)")]

#for feat in medians:
annotators = src_feat.keys()

median_phis_h = {}
median_phis_m_soa = {}
median_phis_m_base = {}

for feature in only_feat:
    phi_h = [list(src_feat[a][feature]) for a in annotators
                                                if a.startswith('Humans')]
    median_phis_h[feature] = np.median(
                            list(itertools.chain.from_iterable(phi_h))
                            )  # General human reference
    phi_m_soa = [list(src_feat[a][feature]) for a in annotators
                                                if a.startswith('Machines_soa_')]
    median_phis_m_soa[feature] = np.median(
                            list(itertools.chain.from_iterable(phi_m_soa))
                            )  # General human reference

    phi_m_base = [list(src_feat[a][feature]) for a in annotators
                                                if a.startswith('Machines_base_')]
    median_phis_m_base[feature] = np.median(
                            list(itertools.chain.from_iterable(phi_m_base))
                            )  # General human reference

if args.sp_bx == 'sp':
    spectrum_base = feature_spectrum(median_phis_h, median_phis_m_base)
    spectrum_soa = feature_spectrum(median_phis_h, median_phis_m_soa)

    spectra_base = sorted(spectrum_base.items(), key=operator.itemgetter(1))
    spectra_soa = [(f, spectrum_soa[f]) for f, _ in spectra_base]

    x, y_base = zip(*spectra_base)
    _, y_soa = zip(*spectra_soa)

    #fig, ax = plt.subplots()

    plt.xlabel('Compared features', fontsize=14)#, color='blue')
    plt.ylabel('Spectrum magnitude', fontsize=14)
    plt.title("Feature spectrum for State-of-the-Art"
            " and Baseline machine-made summaries", fontsize=11)
    plt.grid(True)
    plt.annotate('State-of-the-Art summaries', xy=(18, 2), xytext=(20, -4.8),
            arrowprops=dict(facecolor='black', shrink=0.05),
            )

    plt.annotate('Baseline summaries', xy=(20, 6), xytext=(15, 9),
            arrowprops=dict(facecolor='black', shrink=0.05),
            )

    plt.text(23, -1.0, "Human summaries")

    plt.margins(0.08)
    plt.xticks(range(len(x)), x, rotation='vertical', fontsize=10)
#fig = plt.figure(figsize=(18, 18))
#plt.figure(tight_layout=True);
    plt.tight_layout()
    plt.locator_params(axis='y', nbins=20, tight=True)
    plt.plot(range(len(x)), y_soa, range(len(x)), y_base, range(len(x)),
         [0.0]*len(x), 'r--', linewidth=2.0)
#ax.errorbar(range(len(x)), y, xerr=0.2, yerr=0.4)
    plt.show()

else:
    
    for feat in table_all:
        annotators_feat={}
        if feat not in non_feats and feat in only_feat:
            for i, a in enumerate(annotators):
                annotators_feat[a]=list(src_feat[a][feat])
                m = np.median(annotators_feat[a])
                annotators_feat[a] = annotators_feat[a] + [m] * (docs_per_annotator 
                                                         - len(annotators_feat[a]))

            annotators_feat = pd.DataFrame(annotators_feat)
            ax=annotators_feat.plot(kind="box", vert=vertical, figsize=(17,15))
            ax.set_title(loc='center', label="Feature "+feat+
                        ": boxplots human/machine summarizers")
        #ax.set_aspect(0.33)
            ax.set_aspect(aspect)
            ax.grid(color='r', linestyle='dashed', axis='y')
            ax.set_xticklabels(ax.get_xticklabels(),rotation=90)
            ax.set_xlabel("Frequency within 100-word summaries")
            for box in ax.lines: box.set_linewidth(linewidth)
            print "Saving image: %s ..." % (directory+"figures/Feature_"+feat+
                    "_all_box_machine-human_samp_"+str(docs_per_annotator)+".png")

            plt.savefig(directory+"figures/Feature_"+feat+
                        "_all_box_machine-human_samp_"+str(docs_per_annotator)+".png")
        #plt.savefig(feat+"_all_box_machine-human_annotators.png")
            plt.clf()
            plt.close("all")

        #plt.errorbar(range(len(annotators)), data_m, yerr=ss.t.ppf(0.95, data_sd)*data_sd)
        #plt.xlim((-1,len(annotators)+1))