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Rewritten in go and python

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This commit is contained in:
Aykhan Shahsuvarov
2024-11-06 01:25:27 +04:00
parent 9f22d9678d
commit d8449237bb
50 changed files with 3824 additions and 879 deletions
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4
recommender/.dockerignore Normal file
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.venv
.ipynb_checkpoints
__pycache__
*.ipynb

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recommender/.gitignore vendored Normal file
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.venv
.ipynb_checkpoints
__pycache__
*.ipynb

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recommender/.python-version Normal file
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3.12

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recommender/Dockerfile Normal file
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FROM python:3.12.3-slim-bookworm
ENV UV_COMPILE_BYTECODE=1
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
RUN apt-get update && apt-get install -y curl
ENV GRPC_HEALTH_PROBE_VERSION=v0.4.35
RUN curl -L -o /bin/grpc_health_probe \
https://github.com/grpc-ecosystem/grpc-health-probe/releases/download/${GRPC_HEALTH_PROBE_VERSION}/grpc_health_probe-linux-amd64 && \
chmod +x /bin/grpc_health_probe
WORKDIR /app
COPY . .
RUN uv sync --frozen

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recommender/config.py Normal file
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import os
def get_postgres_dsn():
user = os.getenv('POSTGRES_USER', None)
if user is None:
raise ValueError('POSTGRES_USER is not set')
password = os.getenv('POSTGRES_PASSWORD', None)
if password is None:
raise ValueError('POSTGRES_PASSWORD is not set')
host = os.getenv('POSTGRES_HOST', None)
if host is None:
raise ValueError('POSTGRES_HOST is not set')
port = os.getenv('POSTGRES_PORT', None)
if port is None:
raise ValueError('POSTGRES_PORT is not set')
try:
port = int(port)
except ValueError:
raise ValueError('POSTGRES_PORT is not an integer')
dbname = os.getenv('POSTGRES_DB', None)
if dbname is None:
raise ValueError('POSTGRES_DB is not set')
return f'postgres://{user}:{password}@{host}:{port}/{dbname}?sslmode=disable'
def get_grpc_port():
port = os.getenv('GRPC_PORT', None)
if port is None:
raise ValueError('GRPC_PORT is not set')
try:
port = int(port)
except ValueError:
raise ValueError('GRPC_PORT is not an integer')
return port

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recommender/main.py Normal file
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from sys import path
path.append('./proto')
from concurrent import futures
from time import sleep
import threading
from recommend import Recommender, Weight, Filter
from config import get_postgres_dsn, get_grpc_port
import psycopg2
from proto import recommender_pb2, recommender_pb2_grpc
import grpc
from grpc_reflection.v1alpha import reflection
from grpc_health.v1 import health
from grpc_health.v1 import health_pb2
from grpc_health.v1 import health_pb2_grpc
postgres_dsn = get_postgres_dsn()
class RecommenderServicer(recommender_pb2_grpc.RecommenderServicer):
def GetRecommendations(self, request: recommender_pb2.Request, context):
try:
recommender = Recommender(
filter_=Filter(
min_votes=request.filter.min_votes if request.filter.HasField('min_votes_oneof') else None,
max_votes=request.filter.max_votes if request.filter.HasField('max_votes_oneof') else None,
min_year=request.filter.min_year if request.filter.HasField('min_year_oneof') else None,
max_year=request.filter.max_year if request.filter.HasField('max_year_oneof') else None,
min_rating=request.filter.min_rating if request.filter.HasField('min_rating_oneof') else None,
max_rating=request.filter.max_rating if request.filter.HasField('max_rating_oneof') else None
),
weight=Weight(
year=request.weight.year,
rating=request.weight.rating,
genres=request.weight.genres,
nconsts=request.weight.nconsts
)
)
except ValueError as e:
context.set_code(grpc.StatusCode.INVALID_ARGUMENT)
context.set_details(str(e))
return recommender_pb2.Response()
except Exception as e:
context.set_code(grpc.StatusCode.INTERNAL)
context.set_details(str(e))
return recommender_pb2.Response()
with psycopg2.connect(dsn=postgres_dsn) as conn:
try:
data = recommender.get_recommendations(conn, request.tconsts, request.n)
except ValueError as e:
context.set_code(grpc.StatusCode.NOT_FOUND)
context.set_details(str(e))
return recommender_pb2.Response()
except Exception as e:
context.set_code(grpc.StatusCode.INTERNAL)
context.set_details(str(e))
return recommender_pb2.Response()
movies = []
for k, v in data.items():
movies.append(
recommender_pb2.RecommendedMovie(
tconst=k,
weights=v
)
)
return recommender_pb2.Response(movies=movies)
def _toggle_health(health_servicer: health.HealthServicer, service: str):
next_status = health_pb2.HealthCheckResponse.SERVING
while True:
if next_status == health_pb2.HealthCheckResponse.SERVING:
next_status = health_pb2.HealthCheckResponse.NOT_SERVING
else:
next_status = health_pb2.HealthCheckResponse.SERVING
health_servicer.set(service, next_status)
sleep(5)
def _configure_health_server(server: grpc.Server):
health_servicer = health.HealthServicer(
experimental_non_blocking=True,
experimental_thread_pool=futures.ThreadPoolExecutor(max_workers=10),
)
health_pb2_grpc.add_HealthServicer_to_server(health_servicer, server)
toggle_health_status_thread = threading.Thread(
target=_toggle_health,
args=(health_servicer, "recommender.Recommender"),
daemon=True,
)
toggle_health_status_thread.start()
def serve():
server = grpc.server(futures.ThreadPoolExecutor(max_workers=100))
recommender_pb2_grpc.add_RecommenderServicer_to_server(RecommenderServicer(), server)
SERVICE_NAMES = (
recommender_pb2.DESCRIPTOR.services_by_name["Recommender"].full_name,
reflection.SERVICE_NAME,
)
reflection.enable_server_reflection(SERVICE_NAMES, server)
server.add_insecure_port(f'[::]:{get_grpc_port()}')
_configure_health_server(server)
server.start()
server.wait_for_termination()
if __name__ == '__main__':
try:
serve()
except KeyboardInterrupt:
print("Shutting down server")

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recommender/proto/recommender_pb2.py Normal file
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# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# NO CHECKED-IN PROTOBUF GENCODE
# source: recommender.proto
# Protobuf Python Version: 5.27.2
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import runtime_version as _runtime_version
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
_runtime_version.ValidateProtobufRuntimeVersion(
_runtime_version.Domain.PUBLIC,
5,
27,
2,
'',
'recommender.proto'
)
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x11recommender.proto\x12\x0brecommender\"\xf8\x01\n\x06\x46ilter\x12\x13\n\tmin_votes\x18\x01 \x01(\rH\x00\x12\x13\n\tmax_votes\x18\x02 \x01(\rH\x01\x12\x12\n\x08min_year\x18\x03 \x01(\rH\x02\x12\x12\n\x08max_year\x18\x04 \x01(\rH\x03\x12\x14\n\nmin_rating\x18\x05 \x01(\x02H\x04\x12\x14\n\nmax_rating\x18\x06 \x01(\x02H\x05\x42\x11\n\x0fmin_votes_oneofB\x11\n\x0fmax_votes_oneofB\x10\n\x0emin_year_oneofB\x10\n\x0emax_year_oneofB\x12\n\x10min_rating_oneofB\x12\n\x10max_rating_oneof\"G\n\x06Weight\x12\x0c\n\x04year\x18\x01 \x01(\r\x12\x0e\n\x06rating\x18\x02 \x01(\r\x12\x0e\n\x06genres\x18\x03 \x01(\r\x12\x0f\n\x07nconsts\x18\x04 \x01(\r\"o\n\x07Request\x12\x0f\n\x07tconsts\x18\x01 \x03(\t\x12\t\n\x01n\x18\x02 \x01(\r\x12#\n\x06\x66ilter\x18\x03 \x01(\x0b\x32\x13.recommender.Filter\x12#\n\x06weight\x18\x04 \x01(\x0b\x32\x13.recommender.Weight\"9\n\x08Response\x12-\n\x06movies\x18\x01 \x03(\x0b\x32\x1d.recommender.RecommendedMovie\"3\n\x10RecommendedMovie\x12\x0e\n\x06tconst\x18\x01 \x01(\t\x12\x0f\n\x07weights\x18\x02 \x03(\t2R\n\x0bRecommender\x12\x43\n\x12GetRecommendations\x12\x14.recommender.Request\x1a\x15.recommender.Response\"\x00\x42,Z*github.com/aykhans/movier/server/pkg/protob\x06proto3')
_globals = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'recommender_pb2', _globals)
if not _descriptor._USE_C_DESCRIPTORS:
_globals['DESCRIPTOR']._loaded_options = None
_globals['DESCRIPTOR']._serialized_options = b'Z*github.com/aykhans/movier/server/pkg/proto'
_globals['_FILTER']._serialized_start=35
_globals['_FILTER']._serialized_end=283
_globals['_WEIGHT']._serialized_start=285
_globals['_WEIGHT']._serialized_end=356
_globals['_REQUEST']._serialized_start=358
_globals['_REQUEST']._serialized_end=469
_globals['_RESPONSE']._serialized_start=471
_globals['_RESPONSE']._serialized_end=528
_globals['_RECOMMENDEDMOVIE']._serialized_start=530
_globals['_RECOMMENDEDMOVIE']._serialized_end=581
_globals['_RECOMMENDER']._serialized_start=583
_globals['_RECOMMENDER']._serialized_end=665
# @@protoc_insertion_point(module_scope)

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recommender/proto/recommender_pb2.pyi Normal file
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from google.protobuf.internal import containers as _containers
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from typing import ClassVar as _ClassVar, Iterable as _Iterable, Mapping as _Mapping, Optional as _Optional, Union as _Union
DESCRIPTOR: _descriptor.FileDescriptor
class Filter(_message.Message):
__slots__ = ("min_votes", "max_votes", "min_year", "max_year", "min_rating", "max_rating")
MIN_VOTES_FIELD_NUMBER: _ClassVar[int]
MAX_VOTES_FIELD_NUMBER: _ClassVar[int]
MIN_YEAR_FIELD_NUMBER: _ClassVar[int]
MAX_YEAR_FIELD_NUMBER: _ClassVar[int]
MIN_RATING_FIELD_NUMBER: _ClassVar[int]
MAX_RATING_FIELD_NUMBER: _ClassVar[int]
min_votes: int
max_votes: int
min_year: int
max_year: int
min_rating: float
max_rating: float
def __init__(self, min_votes: _Optional[int] = ..., max_votes: _Optional[int] = ..., min_year: _Optional[int] = ..., max_year: _Optional[int] = ..., min_rating: _Optional[float] = ..., max_rating: _Optional[float] = ...) -> None: ...
class Weight(_message.Message):
__slots__ = ("year", "rating", "genres", "nconsts")
YEAR_FIELD_NUMBER: _ClassVar[int]
RATING_FIELD_NUMBER: _ClassVar[int]
GENRES_FIELD_NUMBER: _ClassVar[int]
NCONSTS_FIELD_NUMBER: _ClassVar[int]
year: int
rating: int
genres: int
nconsts: int
def __init__(self, year: _Optional[int] = ..., rating: _Optional[int] = ..., genres: _Optional[int] = ..., nconsts: _Optional[int] = ...) -> None: ...
class Request(_message.Message):
__slots__ = ("tconsts", "n", "filter", "weight")
TCONSTS_FIELD_NUMBER: _ClassVar[int]
N_FIELD_NUMBER: _ClassVar[int]
FILTER_FIELD_NUMBER: _ClassVar[int]
WEIGHT_FIELD_NUMBER: _ClassVar[int]
tconsts: _containers.RepeatedScalarFieldContainer[str]
n: int
filter: Filter
weight: Weight
def __init__(self, tconsts: _Optional[_Iterable[str]] = ..., n: _Optional[int] = ..., filter: _Optional[_Union[Filter, _Mapping]] = ..., weight: _Optional[_Union[Weight, _Mapping]] = ...) -> None: ...
class Response(_message.Message):
__slots__ = ("movies",)
MOVIES_FIELD_NUMBER: _ClassVar[int]
movies: _containers.RepeatedCompositeFieldContainer[RecommendedMovie]
def __init__(self, movies: _Optional[_Iterable[_Union[RecommendedMovie, _Mapping]]] = ...) -> None: ...
class RecommendedMovie(_message.Message):
__slots__ = ("tconst", "weights")
TCONST_FIELD_NUMBER: _ClassVar[int]
WEIGHTS_FIELD_NUMBER: _ClassVar[int]
tconst: str
weights: _containers.RepeatedScalarFieldContainer[str]
def __init__(self, tconst: _Optional[str] = ..., weights: _Optional[_Iterable[str]] = ...) -> None: ...

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recommender/proto/recommender_pb2_grpc.py Normal file
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# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
import warnings
import recommender_pb2 as recommender__pb2
GRPC_GENERATED_VERSION = '1.67.0'
GRPC_VERSION = grpc.__version__
_version_not_supported = False
try:
from grpc._utilities import first_version_is_lower
_version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION)
except ImportError:
_version_not_supported = True
if _version_not_supported:
raise RuntimeError(
f'The grpc package installed is at version {GRPC_VERSION},'
+ f' but the generated code in recommender_pb2_grpc.py depends on'
+ f' grpcio>={GRPC_GENERATED_VERSION}.'
+ f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}'
+ f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.'
)
class RecommenderStub(object):
"""Missing associated documentation comment in .proto file."""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.GetRecommendations = channel.unary_unary(
'/recommender.Recommender/GetRecommendations',
request_serializer=recommender__pb2.Request.SerializeToString,
response_deserializer=recommender__pb2.Response.FromString,
_registered_method=True)
class RecommenderServicer(object):
"""Missing associated documentation comment in .proto file."""
def GetRecommendations(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_RecommenderServicer_to_server(servicer, server):
rpc_method_handlers = {
'GetRecommendations': grpc.unary_unary_rpc_method_handler(
servicer.GetRecommendations,
request_deserializer=recommender__pb2.Request.FromString,
response_serializer=recommender__pb2.Response.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'recommender.Recommender', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
server.add_registered_method_handlers('recommender.Recommender', rpc_method_handlers)
# This class is part of an EXPERIMENTAL API.
class Recommender(object):
"""Missing associated documentation comment in .proto file."""
@staticmethod
def GetRecommendations(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(
request,
target,
'/recommender.Recommender/GetRecommendations',
recommender__pb2.Request.SerializeToString,
recommender__pb2.Response.FromString,
options,
channel_credentials,
insecure,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
_registered_method=True)

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[project]
name = "movier"
version = "0.1.0"
description = "Movier"
readme = "README.md"
requires-python = "==3.12.3"
dependencies = [
"grpcio>=1.67.0",
"grpcio-reflection==1.67.0",
"grpcio-tools==1.67.0",
"polars==1.12.0",
"psycopg2-binary==2.9.10",
"scikit-learn==1.5.2",
"grpcio-health-checking==1.67.1",
]

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from typing import Any
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import polars as pl
from dataclasses import dataclass
@dataclass
class Filter:
min_votes: int = None
max_votes: int = None
min_year: int = None
max_year: int = None
min_rating: float = None
max_rating: float = None
def __post_init__(self):
if self.min_votes is not None and self.min_votes < 0:
raise ValueError("min_votes should be greater than or equal to 0")
if self.max_votes is not None and self.max_votes < 0:
raise ValueError("max_votes should be greater than or equal to 0")
if self.min_votes is not None and self.max_votes is not None and self.min_votes > self.max_votes:
raise ValueError("min_votes should be less than or equal to max_votes")
if self.min_year is not None and self.min_year < 0:
raise ValueError("min_year should be greater than or equal to 0")
if self.max_year is not None and self.max_year < 0:
raise ValueError("max_year should be greater than or equal to 0")
if self.min_year is not None and self.max_year is not None and self.min_year > self.max_year:
raise ValueError("min_year should be less than or equal to max_year")
if self.min_rating is not None and self.min_rating < 0:
raise ValueError("min_rating should be greater than or equal to 0")
if self.max_rating is not None and self.max_rating < 0:
raise ValueError("max_rating should be greater than or equal to 0")
if self.min_rating is not None and self.max_rating is not None and self.min_rating > self.max_rating:
raise ValueError("min_rating should be less than or equal to max_rating")
@dataclass
class Weight:
year: int = 100
rating: int = 100
genres: int = 100
nconsts: int = 100
def __post_init__(self):
total_sum = 0
total_count = 0
for k, v in self.__dict__.items():
if v < 0:
raise ValueError(f'Weight for {k} must be greater than or equal to 0, got {v}')
if v > 0:
total_sum += v
total_count += 1
if total_sum < 100:
raise ValueError(f'Total sum of weights must be at least 100, got {total_sum}')
if total_count*100 != total_sum:
raise ValueError(f'Total sum of weights must be {total_count*100}, got {total_sum}')
class Recommender:
def __init__(
self,
filter_: Filter = Filter(),
weight: Weight = Weight()
) -> None:
self.filter = filter_
self.weight = weight
self.sql_where_clause = ''
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f"genres != ''")
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f"nconsts != ''")
if filter_.min_votes:
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f'votes >= {filter_.min_votes}')
if filter_.max_votes:
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f'votes <= {filter_.max_votes}')
if filter_.min_year:
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f'year >= {filter_.min_year}')
if filter_.max_year:
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f'year <= {filter_.max_year}')
if filter_.min_rating:
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f'rating >= {filter_.min_rating}')
if filter_.max_rating:
self.sql_where_clause = self.add_sql_where_clause(self.sql_where_clause, f'rating <= {filter_.max_rating}')
def add_sql_where_clause(self, old: str, new: str) -> None:
return f'WHERE {new}' if old == '' else f'{old} AND {new}'
def get_ordered_year_from_sql(self, conn, reference_year: int) -> pl.DataFrame:
"""
Args
----
conn: psycopg2 connection object
reference_year: int - year to sort by closest
Returns
-------
DataFrame:
First sorted by closest year, then by number of votes (descending).
| year_index (uint32) | tconst (str) |
| --- | --- |
| 0 | tt0000001 |
| 1 | tt0000002 |
| 2 | tt0000003 |
| ... | ... |
"""
return pl.read_database(
f"""
SELECT tconst
FROM imdb
{self.sql_where_clause}
ORDER BY ABS(year - {reference_year}), votes DESC
""",
conn, schema_overrides={'tconst': str}
).with_row_index('year_index')
def get_ordered_rating_from_sql(self, conn, reference_rating: int) -> pl.DataFrame:
"""
Args
----
conn: psycopg2 connection object
reference_rating: int - rating to sort by closest
Returns
-------
DataFrame:
First sorted by closest rating, then by number of votes (descending).
| rating_index (uint32) | tconst (str) |
| --- | --- |
| 0 | tt0000001 |
| 1 | tt0000002 |
| 2 | tt0000003 |
| ... | ... |
"""
return pl.read_database(
f"""
SELECT tconst
FROM imdb
{self.sql_where_clause}
ORDER BY ABS(rating - {reference_rating}), votes DESC
""",
conn, schema_overrides={'tconst': str}
).with_row_index('rating_index')
def get_ordered_genres_from_df(self, df: pl.DataFrame, reference_genres: str) -> pl.DataFrame:
"""
Args
----
df: DataFrame
| tconst (str) | genres (str) | votes (uint32) |
| --- | --- | --- |
| tt0000001 | Drama, Romance | 123 |
| tt0000002 | Comedy, Drama | 456 |
| tt0000003 | Action, Drama | 789 |
| ... | ... | ... |
reference_genres: str - genres to calculate cosine similarities
Returns
-------
DataFrame:
First sorted by cosine similarities genres (descending) and then by number of votes (descending).
| genres_index (uint32) | tconst (str) |
| --- | --- |
| 0 | tt0000001 |
| 1 | tt0000002 |
| 2 | tt0000003 |
| ... | ... |
"""
df = df.with_row_index('genres_index')
genres_cv = CountVectorizer(dtype=np.uint8, token_pattern=r"(?u)[\w'-]+")
genres_count_matrix = genres_cv.fit_transform(df['genres'])
genres_sims = cosine_similarity(genres_cv.transform([reference_genres]), genres_count_matrix)[0]
return pl.DataFrame(
{
'tconst': df['tconst'],
'cosine_similarity': genres_sims,
'votes': df['votes']
}, schema={'tconst': str, 'cosine_similarity': pl.Float32, 'votes': pl.UInt32}
).\
sort(['cosine_similarity', 'votes'], descending=True).\
drop(['cosine_similarity', 'votes']).\
with_row_index('genres_index')
def get_ordered_nconsts_from_df(self, df: pl.DataFrame, reference_nconsts: str) -> pl.DataFrame:
"""
Args
----
df: DataFrame
| tconst (str) | nconsts (str) | votes (uint32) |
| --- | --- | --- |
| tt0000001 | nm0000001, nm0000002 | 123 |
| tt0000002 | nm0000001, nm0000003 | 456 |
| tt0000003 | nm0000004, nm0000002 | 789 |
| ... | ... | ... |
reference_nconsts: str - nconsts to calculate cosine similarities
Returns
-------
df: DataFrame
First sorted by cosine similarities of nconsts (descending) and then by number of votes (descending).
| nconsts_index (uint32) | tconst (str) |
| --- | --- |
| 0 | tt0000001 |
| 1 | tt0000002 |
| 2 | tt0000003 |
| ... | ... |
"""
df = df.with_row_index('nconsts_index')
nconsts_cv = CountVectorizer(dtype=np.uint8, token_pattern=r"(?u)[\w'-]+")
nconsts_count_matrix = nconsts_cv.fit_transform(df['nconsts'])
nconsts_sims = cosine_similarity(nconsts_cv.transform([reference_nconsts]), nconsts_count_matrix)[0]
return pl.DataFrame(
{
'tconst': df['tconst'],
'cosine_similarity': nconsts_sims,
'votes': df['votes']
}, schema={'tconst': str, 'cosine_similarity': pl.Float32, 'votes': pl.UInt32}
).\
sort(['cosine_similarity', 'votes'], descending=True).\
drop(['cosine_similarity', 'votes']).\
with_row_index('nconsts_index')
def get_main_df(self, conn) -> pl.DataFrame:
"""
Args
----
conn: psycopg2 connection object
Returns
-------
DataFrame:
| tconst (str) | genres (str) | nconsts (str) | votes (uint32) |
| --- | --- | --- | --- |
| tt0000001 | Drama, Romance | nm0000001, nm0000002 | 123 |
| tt0000002 | Comedy, Drama | nm0000001, nm0000003 | 456 |
| tt0000003 | Action, Drama | nm0000004, nm0000002 | 789 |
| ... | ... | ... | ... |
"""
return pl.read_database(
f"""
SELECT tconst, genres, nconsts, votes
FROM imdb
{self.sql_where_clause}
""", conn, schema_overrides={'tconst': str, 'genres': str, 'nconsts': str, 'votes': pl.UInt32}
)
def get_row_by_tconst(self, conn, tconst: str) -> dict[str, Any]:
"""
Args
----
conn: psycopg2 connection object
tconst: str - tconst to get row from database
Returns
-------
dict: row from database
{
'tconst': str,
'year': int,
'genres': str,
'nconsts': str,
'rating': float,
'votes': int
}
Raises
------
ValueError: if tconst is not found in database
"""
with conn.cursor() as cursor:
cursor.execute(
f"""
SELECT tconst, year, genres, nconsts, rating, votes
FROM imdb
WHERE tconst = '{tconst}'
"""
)
row = cursor.fetchone()
if row is None:
raise ValueError(f"tconst '{tconst}' not found")
return {cursor.description[i][0]: value for i, value in enumerate(row)}
def set_average(self, column_name: str, features: list[str], merged_df: pl.DataFrame) -> pl.DataFrame:
"""
Args
----
column_name: str - name of the column to store the average
features: list[str] - list of features to calculate the average
merged_df: DataFrame - merged DataFrame of all features
Returns
-------
DataFrame: Same DataFrame with the argument column_name added to it with the average of all features
"""
average = merged_df[f'{features[0]}_index'] * self.weight.__getattribute__(features[0])
for feature in features[1:]:
average += merged_df[f'{feature}_index'] * self.weight.__getattribute__(feature)
return merged_df.with_columns(**{column_name: (average / (len(features) * 100))})
def get_single_recommendation(self, conn, tconst: str, features: list[str]) -> pl.DataFrame:
"""
Args
----
conn: psycopg2 connection object
tconst: str - tconst to get recommendations
features: list[str] - list of features to calculate the average
Returns
-------
DataFrame: DataFrame with the average of all features
Raises
------
ValueError: if no recommendations found
"""
reference_row = self.get_row_by_tconst(conn, tconst)
trained: dict[str, pl.DataFrame] = {}
if 'year' in features:
df = self.get_ordered_year_from_sql(conn, reference_year=reference_row['year'])
if len(df) > 0:
trained['year'] = df
if 'rating' in features:
df = self.get_ordered_rating_from_sql(conn, reference_rating=reference_row['rating'])
if len(df) > 0:
trained['rating'] = df
if 'genres' in features or 'nconsts' in features:
main_df = self.get_main_df(conn)
if len(main_df) > 0:
if 'genres' in features:
trained['genres'] = self.get_ordered_genres_from_df(
pl.DataFrame(
{
'tconst': main_df['tconst'],
'genres': main_df['genres'],
'votes': main_df['votes']
}
), reference_genres=reference_row['genres']
)
if 'nconsts' in features:
trained['nconsts'] = self.get_ordered_nconsts_from_df(
pl.DataFrame(
{
'tconst': main_df['tconst'],
'nconsts': main_df['nconsts'],
'votes': main_df['votes']
}
), reference_nconsts=reference_row['nconsts']
)
if len(trained) == 0:
raise ValueError("No recommendations found, try changing the filter or weight")
if len(features) > 1:
merged = pl.concat(trained.values(), how='align')
return self.set_average(
"average", features=features, merged_df=merged
)
else:
trained_df = trained[features[0]]
return trained_df.with_columns(
average=trained_df[f'{features[0]}_index']
)
def get_recommendations(self, conn, tconsts: list[str], n: int = 5) -> dict[str, list[str]]:
"""
Args
----
conn: psycopg2 connection object
tconsts: list[str] - list of tconsts to get recommendations
n: int - number of recommendations to get
Returns
-------
list[dict[str, list[str]]]: list of dictionaries with tconst (ascending)
as key and list of weights of columns as value (ascending)
"""
self.sql_where_clause = self.add_sql_where_clause(
self.sql_where_clause,
f"tconst NOT IN ({', '.join(f"'{tconst}'" for tconst in tconsts)})"
)
features: list[str] = []
if self.weight.year > 0:
features.append('year')
if self.weight.rating > 0:
features.append('rating')
if self.weight.genres > 0:
features.append('genres')
if self.weight.nconsts > 0:
features.append('nconsts')
if len(tconsts) == 1:
merged_df = self.get_single_recommendation(conn, tconsts[0], features).sort('average')[:n]
responses: dict[str, list[str]] = dict()
for row in merged_df.rows(named=True):
row.pop('average')
t: str = row.pop('tconst')
for f in features:
row[f] = row[f"{f}_index"] / self.weight.__getattribute__(f)
row.pop(f"{f}_index")
weights: list[str] = [column for column, _ in sorted(row.items(), key=lambda item: item[1])]
responses[t] = weights
return responses
else:
trained_dfs: dict[str, pl.DataFrame] = {}
for tconst in tconsts:
df = self.get_single_recommendation(conn, tconst, features)
trained_dfs[tconst] = pl.DataFrame({
'tconst': df['tconst'],
f"{tconst}_average": df['average']
})
merged_df: pl.DataFrame = pl.concat(trained_dfs.values(), how='align')
all_average = merged_df[f"{tconsts[0]}_average"]
for tconst in tconsts[1:]:
all_average += merged_df[f"{tconst}_average"]
merged_df = merged_df.with_columns(all_average=all_average / len(tconsts)).sort('all_average')[:n]
responses: dict[str, list[str]] = dict()
for row in merged_df.rows(named=True):
row.pop('all_average')
curretn_tconst: str = row.pop('tconst')
for tconst in tconsts:
row[tconst] = row[f"{tconst}_average"]
row.pop(f"{tconst}_average")
weights: list[str] = [column for column, _ in sorted(row.items(), key=lambda item: item[1])]
responses[curretn_tconst] = weights
return responses

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