#!/usr/bin/env python
"""
    recommender suite - recommender experiments suite 
"""
__author__ = "Tassia Camoes Araujo <tassia@gmail.com>"
__copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo"
__license__ = """
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""

import sys
sys.path.insert(0,'../')
from config import Config
from data import PopconXapianIndex, PopconSubmission, AppAptXapianIndex
from recommender import Recommender
from user import LocalSystem, User
from evaluation import *
import logging
import random
import Gnuplot

#iterations = 3
#sample_proportions = [0.9]
#weighting = [('bm25',1.2)]
#collaborative = ['knn']
#content_based = []
#hybrid = ['knnco']
#profile_size = [50,100]
#popcon_size = ["1000"]
#neighbors = [50]

iterations = 10
sample_proportions = [0.5, 0.6, 0.7, 0.8, 0.9]
weighting = [('bm25',1.2), ('bm25',1.6), ('bm25',2.0), ('trad',0)]
content_based = ['cb','cbt','cbd','cbh','cb_eset','cbt_eset','cbd_eset','cbh_eset']
collaborative = ['knn_eset','knn','knn_plus']
hybrid = ['knnco','knnco_eset']

profile_size = range(20,100,20)
#popcon_size = [1000,10000,50000,'full']
neighbors = range(10,510,50)

def write_recall_log(label,n,sample,recommendation,profile_size,repo_size,log_file):
    # Write recall log
    output = open(("%s-%d" % (log_file,n)),'w')
    output.write("# %s-n\n" % label["description"])
    output.write("# %s-%d\n" % (label["values"],n))
    output.write("\n%d %d %d\n" % \
                 (repo_size,profile_size,len(sample)))
    if hasattr(recommendation,"ranking"):
        notfound = []
        ranks = []
        for pkg in sample.keys():
            if pkg in recommendation.ranking:
                ranks.append(recommendation.ranking.index(pkg))
            else:
                notfound.append(pkg)
        for r in sorted(ranks):
            output.write(str(r)+"\n")
        if notfound:
            output.write("Out of recommendation:\n")
            for pkg in notfound:
                output.write(pkg+"\n")
    output.close()

def plot_summary(precision,recall,f1,f05,accuracy,log_file):
    # Plot metrics summary
    g = Gnuplot.Gnuplot()
    g('set style data lines')
    g.xlabel('Recommendation size')
    g.title("Setup: %s" % log_file.split("/")[-1])
    g.plot(Gnuplot.Data(accuracy,title="Accuracy"),
           Gnuplot.Data(precision,title="Precision"),
           Gnuplot.Data(recall,title="Recall"),
           Gnuplot.Data(f1,title="F_1"),
           Gnuplot.Data(f05,title="F_0.5"))
    g.hardcopy(log_file+".png",terminal="png")
    g.hardcopy(log_file+".ps",terminal="postscript",enhanced=1,color=1)
    g('set logscale x')
    g('replot')
    g.hardcopy(log_file+"-logscale.png",terminal="png")
    g.hardcopy(log_file+"-logscale.ps",terminal="postscript",enhanced=1,color=1)

def get_label(cfg,sample_proportion):
    label = {}
    if cfg.strategy in content_based:
        label["description"] = "strategy-filter-profile-k1_bm25-sample"
        label["values"] = ("%s-profile%d-%s-kbm%.1f-sample%.1f" %
                           (cfg.strategy,cfg.profile_size,
                            cfg.pkgs_filter.split("/")[-1],
                            cfg.bm25_k1,sample_proportion))
    elif cfg.strategy in collaborative:
       label["description"] = "strategy-knn-filter-k1_bm25-sample"
       label["values"] = ("%s-k%d-%s-kbm%.1f-sample%.1f" %
                          (cfg.strategy,cfg.k_neighbors,
                           cfg.pkgs_filter.split("/")[-1],
                           cfg.bm25_k1,sample_proportion))
    elif cfg.strategy in hybrid:
       label["description"] = "strategy-knn-filter-profile-k1_bm25-sample"
       label["values"] = ("%s-k%d-profile%d-%s-kbm%.1f-sample%.1f" %
                          (cfg.strategy,cfg.k_neighbors,cfg.profile_size,
                           cfg.pkgs_filter.split("/")[-1],
                           cfg.bm25_k1,sample_proportion))
    else:
        print "Unknown strategy"
    return label

class ExperimentResults:
    def __init__(self,repo_size):
        self.repository_size = repo_size
        self.accuracy = {}
        self.precision = {}
        self.recall = {}
        self.f1 = {}
        self.f05 = {}
        points = [1]+range(10,200,10)+range(200,self.repository_size,100)
        for size in points:
            self.accuracy[size] = []
            self.precision[size] = []
            self.recall[size] = []
            self.f1[size] = []
            self.f05[size] = []

    def add_result(self,ranking,sample):
        for size in self.accuracy.keys():
            predicted = RecommendationResult(dict.fromkeys(ranking[:size],1))
            real = RecommendationResult(sample)
            evaluation = Evaluation(predicted,real,self.repository_size)
            self.accuracy[size].append(evaluation.run(Accuracy()))
            self.precision[size].append(evaluation.run(Precision()))
            self.recall[size].append(evaluation.run(Recall()))
            self.f1[size].append(evaluation.run(F_score(1)))
            self.f05[size].append(evaluation.run(F_score(0.5)))

    def get_precision_summary(self):
        summary = [[size,sum(values)/len(values)] for size,values in self.precision.items()]
        return sorted(summary)

    def get_recall_summary(self):
        summary = [[size,sum(values)/len(values)] for size,values in self.recall.items()]
        return sorted(summary)

    def get_f1_summary(self):
        summary = [[size,sum(values)/len(values)] for size,values in self.f1.items()]
        return sorted(summary)

    def get_f05_summary(self):
        summary = [[size,sum(values)/len(values)] for size,values in self.f05.items()]
        return sorted(summary)

    def get_accuracy_summary(self):
        summary = [[size,sum(values)/len(values)] for size,values in self.accuracy.items()]
        return sorted(summary)

    def best_precision(self):
        size = max(self.precision, key = lambda x: max(self.precision[x]))
        return (size,max(self.precision[size]))

    def best_f1(self):
        size = max(self.f1, key = lambda x: max(self.f1[x]))
        return (size,max(self.f1[size]))

    def best_f05(self):
        size = max(self.f05, key = lambda x: max(self.f05[x]))
        return (size,max(self.f05[size]))

def run_strategy(cfg,user):
    for weight in weighting:
        cfg.weight = weight[0]
        cfg.bm25_k1 = weight[1]
        rec = Recommender(cfg)
        repo_size = rec.items_repository.get_doccount()
        for proportion in sample_proportions:
            results = ExperimentResults(repo_size)
            label = get_label(cfg,proportion)
            log_file = "results/strategies/"+label["values"]
            for n in range(iterations):
                # Fill sample profile
                profile_size = len(user.pkg_profile)
                item_score = {}
                for pkg in user.pkg_profile:
                    item_score[pkg] = user.item_score[pkg]
                sample = {}
                sample_size = int(profile_size*proportion)
                for i in range(sample_size):
                     key = random.choice(item_score.keys())
                     sample[key] = item_score.pop(key)
                iteration_user = User(item_score)
                recommendation = rec.get_recommendation(iteration_user,repo_size)
                write_recall_log(label,n,sample,recommendation,profile_size,repo_size,log_file)
                if hasattr(recommendation,"ranking"):
                    results.add_result(recommendation.ranking,sample)
            with open(log_file,'w') as f:
                precision_10 = sum(results.precision[10])/len(results.precision[10])
                f1_10 = sum(results.f1[10])/len(results.f1[10])
                f05_10 = sum(results.f05[10])/len(results.f05[10])
                f.write("# %s\n# %s\n\ncoverage %d\n\n" %
                        (label["description"],label["values"],recommendation.size))
                f.write("# best results (recommendation size; metric)\n")
                f.write("precision (%d; %.2f)\nf1 (%d; %.2f)\nf05 (%d; %.2f)\n\n" %
                        (results.best_precision()[0],results.best_precision()[1],
                         results.best_f1()[0],results.best_f1()[1],
                         results.best_f05()[0],results.best_f05()[1]))
                f.write("# recommendation size 10\nprecision (10; %.2f)\nf1 (10; %.2f)\nf05 (10; %.2f)" %
                        (precision_10,f1_10,f05_10))
            precision = results.get_precision_summary()
            recall = results.get_recall_summary()
            f1 = results.get_f1_summary()
            f05 = results.get_f05_summary()
            accuracy = results.get_accuracy_summary()
            plot_summary(precision,recall,f1,f05,accuracy,log_file)

def run_content(user,cfg):
    for strategy in content_based:
        cfg.strategy = strategy
        for size in profile_size:
            cfg.profile_size = size
            run_strategy(cfg,user)

def run_collaborative(user,cfg):
    popcon_desktopapps = cfg.popcon_desktopapps
    popcon_programs = cfg.popcon_programs
    for strategy in collaborative:
        cfg.strategy = strategy
        for k in neighbors:
            cfg.k_neighbors = k
            #for size in popcon_size:
            #    if size:
            #        cfg.popcon_desktopapps = popcon_desktopapps+"_"+size
            #        cfg.popcon_programs = popcon_programs+"_"+size
            run_strategy(cfg,user)

def run_hybrid(user,cfg):
    popcon_desktopapps = cfg.popcon_desktopapps
    popcon_programs = cfg.popcon_programs
    for strategy in hybrid:
        cfg.strategy = strategy
        for k in neighbors:
            cfg.k_neighbors = k
            #for size in popcon_size:
            #    if size:
            #        cfg.popcon_desktopapps = popcon_desktopapps+"_"+size
            #        cfg.popcon_programs = popcon_programs+"_"+size
            for size in profile_size:
                cfg.profile_size = size
                run_strategy(cfg,user)

if __name__ == '__main__':
    #user = LocalSystem()
    #user = RandomPopcon(cfg.popcon_dir,os.path.join(cfg.filters_dir,"desktopapps"))

    cfg = Config()
    user = PopconSystem("/root/.app-recommender/popcon-entries/8b/8b44fcdbcf676e711a153d5db09979d7")
    #user = PopconSystem("/root/.app-recommender/popcon-entries/4a/4a67a295ec14826db2aa1d90be2f1623")
    user.filter_pkg_profile(cfg.pkgs_filter)
    user.maximal_pkg_profile()

    if "content" in sys.argv or len(sys.argv)<2:
        run_content(user,cfg)
    if "collaborative" in sys.argv or len(sys.argv)<2:
        run_collaborative(user,cfg)
    if "hybrid" in sys.argv or len(sys.argv)<2:
        run_hybrid(user,cfg)