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Uppdatera classer

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jwradhe 2024-10-26 20:43:13 +02:00
parent fc6fe9c449
commit 498e842523
1 changed files with 16 additions and 65 deletions
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79
main.py
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@ -6,28 +6,19 @@ from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
class LoadData:
def __init__(self):
self.data_file = 'data_movies_series.csv'
self.data = None
self.loaded_datasets = []
def check_data(self):
if os.path.isfile(self.data_file):
self.load_data()
return self.data
else:
self.create_data()
if self.data is not None and not self.data.empty:
self.clean_data()
self.save_data()
num_rows = self.data.shape[0]
print(f'{num_rows} titles loaded successfully.')
return self.data
else:
print("Error: No data was created. Please check the dataset files.")
return None
self.create_data()
self.clean_data()
num_rows = self.data.shape[0]
print(f'{num_rows} titles loaded successfully.')
return self.data
def clean_text(self, text):
if isinstance(text, str):
@ -37,14 +28,7 @@ class LoadData:
return cleaned.strip()
return ''
def clean_data(self):
string_columns = self.data.select_dtypes(include=['object'])
self.data[string_columns.columns] = string_columns.apply(lambda col: col.map(self.clean_text, na_action='ignore'))
self.data = self.data[~self.data['title'].str.strip().isin(['', ':'])]
print(f'Data cleaned successfully.')
def load_dataset(self, dataset_path, stream):
print(f'dataset/{dataset_path}')
try:
df = pd.read_csv(f'dataset/{dataset_path}')
df['stream'] = stream
@ -76,16 +60,13 @@ class LoadData:
df_all = df_all.infer_objects(copy=False)
self.data = df_all
print(f'Data from {", ".join(self.loaded_datasets)} loaded successfully.')
print(f'Data from {", ".join(self.loaded_datasets)} imported.')
def save_data(self):
self.data.to_csv(self.data_file, index=False)
print(f'Data saved to {self.data_file} successfully.')
def load_data(self):
self.data = pd.read_csv(self.data_file)
num_rows = self.data.shape[0]
print(f'{num_rows} titles loaded successfully.')
def clean_data(self):
string_columns = self.data.select_dtypes(include=['object'])
self.data[string_columns.columns] = string_columns.apply(lambda col: col.map(self.clean_text, na_action='ignore'))
self.data = self.data[~self.data['title'].str.strip().isin(['', ':'])]
print(f'Data cleaned')
class UserData:
@ -98,37 +79,6 @@ class UserData:
return self.user_data.lower()
class Search:
def __init__(self, data):
self.data = data
self.preprocess()
def preprocess(self):
self.description_vectorizer = TfidfVectorizer(stop_words='english')
self.description_matrix = self.description_vectorizer.fit_transform(self.data['description'].fillna(''))
self.onehot_encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)
genres_type_matrix = self.onehot_encoder.fit_transform(self.data[['genres', 'type']].fillna(''))
self.feature_matrix = np.hstack([
self.description_matrix.toarray(),
genres_type_matrix,
self.data[['release_year']].fillna(0).to_numpy()
])
def search(self, query, top_n=20):
query_vec = self.description_vectorizer.transform([query])
if hasattr(query_vec, "toarray"):
query_vec = query_vec.toarray()
similarity = cosine_similarity(query_vec, self.description_matrix).flatten()
top_indices = similarity.argsort()[-top_n:][::-1]
return self.data.iloc[top_indices][['title', 'genres', 'type', 'release_year', 'stream','description']]
class Recommendations:
def __init__(self):
@ -136,8 +86,9 @@ class Recommendations:
def get_recommendations(self, user_data, title_data):
if title_data is not None and not title_data.empty:
search_data = Search(title_data)
self.results = search_data.search(user_data)
self.results = "Här ska de komma rekommendationer"
print(self.results)
else:
print("No data available to search.")