/
grapher_gen_results.py
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/
grapher_gen_results.py
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import datetime
import matplotlib.pyplot as plt
import matplotlib.dates
import pandas as pd
import numpy as np
import os.path
class Graphs:
def __init__(self, data_base):
self.data_base = data_base
def overall_results_per(self):
correct = self.data_base["correct"].sum() / len(self.data_base)
wrong = (self.data_base["correct"]==False).sum() / len(self.data_base)
results = [correct, wrong]
outcome = (0.30, 0.50)
fig, ax = plt.subplots()
ax.grid(axis="y", zorder=0, color="#9698A1")
width = 0.15
bars = ax.bar(outcome, results, width, zorder=3)
bars[0].set_color('lightseagreen')
bars[1].set_color('coral')
ax.set_xticks((0.375, 0.575))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticklabels(('Right','Wrong'))
fig.suptitle("Percentage Right/Wrong", fontsize=15)
#plt.show()
def results_per_gen(self):
grouped = (self.data_base["correct"]==True).groupby(self.data_base["article"])
correct = grouped.mean()
incorrect = 1 - grouped.mean()
gender_track = correct.append(incorrect)
fig, ax = plt.subplots()
ind_der = (1, 1.25)
ind_die = (1.75, 2)
ind_das = (2.50, 2.75)
width = 0.25
ax.grid(axis="y", zorder=0, color="#9698A1")
bars_der = ax.bar(ind_der, gender_track["der"], width)
bars_der[0].set_color('lightseagreen')
bars_der[1].set_color('coral')
bars_die = ax.bar(ind_die, gender_track["die"], width)
bars_die[0].set_color('lightseagreen')
bars_die[1].set_color('coral')
bars_das = ax.bar(ind_das, gender_track["das"], width)
bars_das[0].set_color('lightseagreen')
bars_das[1].set_color('coral')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks((1.25, 2, 2.75))
ax.set_xticklabels(('der','die', 'das'))
ax.set_xlim([0.9, 3.1])
fig.suptitle("Percentage Right/Wrong by Gender", fontsize=15)
# plt.show()
def results_per_date(self):
grouped = (self.data_base["correct"]==True).groupby(self.data_base["date"])
correct = grouped.mean()
wrong = 1 - grouped.mean()
data_date = correct.append(wrong)
fig, ax = plt.subplots()
x_loc = np.array([0, 0.15])
ax.grid(axis="y", zorder=0, color="#9698A1")
ticks = []
dates_played = []
for date in pd.unique(data_date.index):
width = 0.15
x_loc += 0.75
bars_date = ax.bar(x_loc, data_date[date], width, zorder=3)
bars_date[0].set_color('lightseagreen')
bars_date[1].set_color('coral')
ticks.append((width+x_loc)[0])
dates_played.append(datetime.datetime.strptime(date, '%d-%m-%Y' ).strftime("%d %b"))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks(ticks)
ax.set_xticklabels(dates_played)
fig.suptitle("Right/Wrong by date", fontsize=15)
ax.set_xlim([ax.get_xlim()[0], ax.get_xlim()[1]+0.15])
# plt.show()
def wrong_rank(self):
wrong_words = self.data_base[self.data_base["correct"]==False]
wrong_by_word = wrong_words.groupby(wrong_words["wort"])
func = {"correct": "count", "article": lambda x: x.iloc[0]} ## "article": Getting the first value of entry
wrong_by_word = wrong_by_word.agg(func).rename(columns = {"correct": "wrong"})
wrong_by_word = wrong_by_word.reset_index()
wrong_ones_sort = wrong_by_word.sort_values(['wrong', 'wort'], ascending = [0, 0])
first_twenty_wrong = wrong_ones_sort[0:20]
col_num = np.arange(first_twenty_wrong.shape[0])
fig, ax = plt.subplots(figsize=(8, 6))
width = 0.65
bars_worng = ax.bar(col_num, first_twenty_wrong["wrong"], width, color= 'powderblue'
, linewidth=0.5)
ax.set_xlim([-0.5, ax.get_xlim()[1]])
ax.set_ylim([0, ax.get_ylim()[1] +1])
ax.set_title('Ranking of wrong words', fontsize=15)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(axis="y", zorder=0, color="#9698A1")
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_yticks(np.arange(first_twenty_wrong.iloc[-1]["wrong"], first_twenty_wrong.iloc[0]["wrong"]+2, 1.0))
ax.set_xticks(col_num + 0.325)
ax.set_xticklabels(list(first_twenty_wrong['wort']))
plt.setp(ax.get_xticklabels(), rotation=90)
for article in range(len(list(first_twenty_wrong['article']))):
ax.text(article + 0.4, 0.15 , list(first_twenty_wrong['article'])[article], rotation= 90,
horizontalalignment='center',
verticalalignment='center', fontsize=12)
# plt.tight_layout()
# plt.show()
def daily_stats(self):
"""Overall right/wrong percent by date"""
grouped = (self.data_base["correct"]==True).groupby(self.data_base["date"])
correct = grouped.mean().reset_index()
correct["wrong"] = 1 - correct["correct"]
correct["date"] = pd.to_datetime(correct["date"], dayfirst=True)
correct = correct.sort_values("date")
fig, ax = plt.subplots()
left_limit = (datetime.datetime.strptime(pd.unique(self.data_base["date"])[0], '%d-%m-%Y' ) -
datetime.timedelta(days= 1)).date()
right_limit = (datetime.datetime.strptime(pd.unique(self.data_base["date"])[-1], '%d-%m-%Y' )
+ datetime.timedelta(days=1)).date()
ax.plot(correct['date'], correct['correct'], marker = '.', color='lightseagreen',
ms = 15, lw = 2, linestyle= '-' , label="correct" )
ax.plot(correct['date'], correct['wrong'], color='coral', marker = '.',
ms = 15, lw = 2, linestyle= '-', label="wrong" )
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.grid(axis="y", zorder=0, color="#9698A1")
ax.set_xticks((correct['date'].values))
ax.set_xlim([left_limit, right_limit])
ax.set_ylim([0., 1.])
ax.legend(loc='upper right').get_frame().set_alpha(0.3)
ax.set_title('Daily Stats', fontsize=15)
ax.set_xticklabels(correct['date'].map(lambda x: x.strftime("%d %b")))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d \n %b'))
def daily_stats_per_gender(self):
"""Overall right/wrong percent by date and gender"""
grouped = (self.data_base["correct"]==True).groupby(
[self.data_base["date"], self.data_base["article"]])
correct_gender = grouped.mean().reset_index()
correct_gender["wrong"] = 1 - correct_gender["correct"]
correct_gender = correct_gender.set_index(["date", "article"]).unstack().reset_index()
correct_gender["date"] = pd.to_datetime(correct_gender["date"], dayfirst=True)
correct_gender = correct_gender.sort_values("date")
left_limit = (datetime.datetime.strptime(pd.unique(self.data_base["date"])[0], '%d-%m-%Y' ) -
datetime.timedelta(days= 1)).date()
right_limit = (datetime.datetime.strptime(pd.unique(self.data_base["date"])[-1], '%d-%m-%Y' )
+ datetime.timedelta(days=1)).date()
fig= plt.figure()
ax = fig.add_axes([0.1, 0.2, 0.85, 0.70])
lab_desc, lab_chars = [], []
gen_spec = {"der":"#6191C5" , "die":"#D56054" , "das": "#69B17D"}
for gender, color_gen in gen_spec.items():
ax.plot(correct_gender["date"], correct_gender["correct"][gender], color= color_gen, marker = '.',
ms = 12, lw = 2, linestyle= '-' )
ax.plot(correct_gender["date"], correct_gender["wrong"][gender], color= color_gen, marker = '.',
ms = 12, lw = 2, linestyle= '--')
leg_char_corr = plt.Line2D((0,1),(0,0), color=color_gen, marker='.', linestyle='-')
leg_char_wrong = plt.Line2D((0,1),(0,0), color=color_gen, marker='.', linestyle='--')
lab_chars.extend([leg_char_corr, leg_char_wrong])
lab_desc.extend([gender + " right", gender + " wrong"])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.grid(axis="y", zorder=0, color="#9698A1")
ax.set_xticks((correct_gender['date'].values))
ax.set_xlim([left_limit, right_limit])
ax.set_ylim([0., 1.1])
ax.set_title('Daily Stats per gender', fontsize=15)
ax.set_xticklabels(correct_gender['date'].map(lambda x: x.strftime("%d %b")))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d \n %b'))
ax.legend(lab_chars, lab_desc, loc='upper center',
bbox_to_anchor=(0.5, -0.15), fancybox=True, shadow=True, ncol=3)
def generate_report(self):
if not os.path.exists("img"):
os.makedirs("img")
self.overall_results_per()
plt.savefig('img/overall_results_per', bbox_inches='tight')
self.results_per_gen()
plt.savefig('img/results_per_gen', bbox_inches='tight')
self.results_per_date()
plt.savefig('img/results_per_date', bbox_inches='tight')
self.wrong_rank()
plt.savefig('img/wrong_rank', bbox_inches='tight')
self.daily_stats()
plt.savefig('img/daily_stats', bbox_inches='tight')
self.daily_stats_per_gender()
plt.savefig('img/daily_stats_per_gender', bbox_inches='tight')