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feat: renamed piviot to pivot and fixed recomended anime not being printed

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This commit is contained in:
Krzysztof Rudnicki 2023-05-29 20:57:27 +02:00
parent 6bdb869e13
commit ffd805450d
1 changed files with 13 additions and 18 deletions
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31
midterm/main.py
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@ -105,7 +105,7 @@ def get_top_ranked(rating_data, data_name, join_table=None, top_data_taken=20):
top_users = group_data_by_rating.dropna().sort_values(ascending=False)[
:top_data_taken]
top_rated = join_table.join(top_users, rsuffix="_r",
how="inner", on=data_name + "_id")
how="inner", on=data_name + "_id")
return top_rated
@ -148,44 +148,39 @@ def preprocessing(rating_data, anime_contact_data, debug=False):
print(rating_data)
get_data_info(rating_data)
piviot_table = rating_data.pivot_table(
pivot_table = rating_data.pivot_table(
index="Name", columns="user_id", values="rating"
).fillna(0)
if debug:
print(piviot_table)
return piviot_table
print(pivot_table)
return pivot_table
def predict(prediction_model, piviot_table):
def predict(prediction_model, pivot_table):
"""
This will choose a random anime name and our prediction_model will predict similar anime.
"""
random_anime = np.random.choice(piviot_table.shape[0])
random_anime = np.random.choice(pivot_table.shape[0])
query = piviot_table.iloc[random_anime, :].values.reshape(1, -1)
query = pivot_table.iloc[random_anime, :].values.reshape(1, -1)
distance, suggestions = prediction_model.kneighbors(query, n_neighbors=6)
random_anime_name = pivot_table.index[random_anime]
for i in range(0, len(distance.flatten())):
if i == 0:
print(f"Recommendations for {0}:\n".format(
piviot_table.index[random_anime]))
print(f"Recommendations for {random_anime_name}:\n")
else:
print(
f"{0}: {1}, with distance of {2}:".format(
i,
piviot_table.index[suggestions.flatten()[i]],
distance.flatten()[i],
)
f"{i}: {pivot_table.index[suggestions.flatten()[i]]}, with distance of {distance.flatten()[i]}:"
)
def create_model(piviot_table):
def create_model(pivot_table):
"""
Creates model based on neaarest neighbor for anime prediction
"""
piviot_table_matrix = csr_matrix(piviot_table.values)
pivot_table_matrix = csr_matrix(pivot_table.values)
model = NearestNeighbors(metric="cosine", algorithm="brute")
model.fit(piviot_table_matrix)
model.fit(pivot_table_matrix)
return model