107 lines
2.7 KiB
Python
107 lines
2.7 KiB
Python
import string
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn import preprocessing
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import wget
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import numpy as np
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.layers import Dropout
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from tensorflow.keras.callbacks import EarlyStopping
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from sklearn.metrics import mean_squared_error, mean_absolute_error
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url = "https://git.wmi.amu.edu.pl/s430705/ium_430705/raw/branch/master/imdb_movies.csv"
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wget.download(url, out="imdb_movies.csv", bar=None)
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movies_data = pd.read_csv("imdb_movies.csv")
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# Drop rows with missing values
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movies_data.dropna(inplace=True)
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#ToDo: Prepare columns for actors, genres, countries
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# Remove not interesting columns
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drop_columns = [
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"title_id",
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"certificate",
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"title",
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"plot",
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"original_title",
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"countries",
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"genres",
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"director",
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"cast",
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"release_date",
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"certificate",
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"plot",
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]
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movies_data.drop(labels=drop_columns, axis=1, inplace=True)
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# Normalize data, lowercase str
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# for column_name in ["original_title", "countries", "genres", "director", "cast"]:
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# movies_data[column_name] = (
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# movies_data[column_name]
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# .str.translate(str.maketrans("", "", string.punctuation))
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# .str.lower()
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# )
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# Remove ',' from votes number and change type to int
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movies_data["votes_number"] = (movies_data["votes_number"].str.replace(",", "")).astype(
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int
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)
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# Normalize number values
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scaler = preprocessing.MinMaxScaler()
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movies_data[["votes_number", "year", "runtime"]] = scaler.fit_transform(
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movies_data[["votes_number", "year", "runtime"]]
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)
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X = movies_data.drop("rating", axis=1)
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Y = movies_data["rating"]
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# Split set to train/test 8:2 ratio
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X_train, X_test, Y_train, Y_test = train_test_split(
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X, Y, test_size=0.2, random_state=42
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)
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# Set up model
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model = Sequential()
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model.add(Dense(8, activation="relu"))
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model.add(Dropout(0.5))
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model.add(Dense(3, activation="relu"))
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model.add(Dropout(0.5))
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model.add(Dense(1))
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model.compile(optimizer="adam", loss="mse")
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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model.fit(
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x=X_train,
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y=Y_train.values,
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validation_data=(X_test, Y_test.values),
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batch_size=128,
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epochs=400,
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callbacks=[early_stop],
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)
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# Predict movie ratings
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predictions = model.predict(X_test)
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pd.DataFrame(predictions).to_csv('results.csv')
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# Compare outputs
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for i, score in enumerate(predictions):
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print(f"Original score: {Y_test.iloc[i]} Predicted score: {score} \n")
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print(f"Difference is : {Y_test.iloc[i] - score}")
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# Evaluate
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print(mean_absolute_error(Y_test, predictions))
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