83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
from sqlite3 import connect
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from rest_framework.decorators import api_view
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from rest_framework.response import Response
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import pandas as pd
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import ras.settings
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import math
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from pymongo import MongoClient
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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from sqlalchemy import create_engine
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from django.db.models import Q
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from django.templatetags.static import static
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import json
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def getBooksData():
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engine = create_engine('mysql+mysqldb://' + ras.settings.DATABASES['default']['USER'] + ':' + ras.settings.DATABASES['default']['PASSWORD'] + '@' + ras.settings.DATABASES['default']['HOST'] + ':3306/' + ras.settings.DATABASES['default']['NAME'])
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df = pd.read_sql('SELECT * FROM api_books ORDER BY readed', engine, parse_dates={'readed': {'format': '%m-%Y'}})
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return df
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def getBookChallenge(year = None):
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engine = create_engine('mysql+mysqldb://' + ras.settings.DATABASES['default']['USER'] + ':' + ras.settings.DATABASES['default']['PASSWORD'] + '@' + ras.settings.DATABASES['default']['HOST'] + ':3306/' + ras.settings.DATABASES['default']['NAME'])
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if(year):
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df = pd.read_sql('SELECT * FROM book_challenge where year = ' + year, engine)
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else:
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df = pd.read_sql('SELECT * FROM book_challenge', engine)
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return df
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def filterData(df, datayear = None):
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df['readed'] = pd.to_datetime(df['readed'], format='%Y-%m-%d')
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df['readed'] = df['readed'].dt.strftime('%m-%Y')
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# Filter data on year
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if datayear and datayear is not None:
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df = df.where(df['readed'].str.contains(datayear))
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return df
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@api_view(['GET'])
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def getStats(request):
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if request.META.get('HTTP_YEAR'):
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data = []
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df = filterData(getBooksData(), request.META.get('HTTP_YEAR'))
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df = df.dropna()
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statsTotalBooks = df['name'].count()
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statsTotalGenres = df['genre'].nunique()
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data.append({
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'totalbooks': statsTotalBooks,
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'totalgenres': statsTotalGenres
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})
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return Response(data[0])
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else:
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return Response("No year header included")
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@api_view(['GET'])
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def predictAmountBooks(request):
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books_data = pd.read_csv("api/static/books_data.csv")
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books_data = books_data.dropna()
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model = LinearRegression()
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X = books_data[['year']]
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Y = books_data['books_read']
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model.fit(X.values, Y.values)
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current_year = 2023
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predict_books = model.predict([[current_year]])
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return Response({
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"year": current_year,
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"amount": math.floor(predict_books[0])
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}) |