Dodanie 'script2.py'

script2.py

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+"""
+Download dataset between 10-20 mb,
+Split it into train/dev/test
+Return dataset info (length, max, min etc.)
+"""
+
+import string
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing
+import wget
+
+url = 'https://git.wmi.amu.edu.pl/s430705/ium_430705/raw/branch/master/imdb_movies.csv'
+wget.download(url, out='imdb_movies.csv', bar=None)
+
+movies_data = pd.read_csv("imdb_movies.csv")
+
+# Drop rows with missing values
+movies_data.dropna(inplace=True)
+
+# Remove not interesting columns
+drop_columns = ["title_id", "certificate", "title", "plot"]
+movies_data.drop(labels=drop_columns, axis=1, inplace=True)
+
+# Normalize data, lowercase str
+for column_name in ["original_title", "countries", "genres", "director", "cast"]:
+    movies_data[column_name] = (
+        movies_data[column_name]
+        .str.translate(str.maketrans("", "", string.punctuation))
+        .str.lower()
+    )
+
+# Remove ',' from votes number and change type to int
+movies_data["votes_number"] = (movies_data["votes_number"].str.replace(",", "")).astype(
+    int
+)
+
+# Normalize number values
+scaler = preprocessing.MinMaxScaler()
+movies_data[["rating", "votes_number", "year", "runtime"]] = scaler.fit_transform(
+    movies_data[["rating", "votes_number", "year", "runtime"]]
+)
+
+# Split set to train/dev/test 6:2:2 ratio and save to .csv file
+train, dev = train_test_split(movies_data, train_size=0.6, test_size=0.4, shuffle=True)
+dev, test = train_test_split(dev, train_size=0.5, test_size=0.5, shuffle=True)
+
+train.to_csv("train.csv")
+dev.to_csv("dev.csv")
+test.to_csv("test.csv")
+
+# Get length of given sets
+print(f"Test dataset length: {len(test)}")
+print(f"Dev dataset length: {len(dev)}")
+print(f"Train dataset length: {len(train)}")
+print(f"Whole dataset length: {len(movies_data)}, \n")
+
+# Print information of given columns
+for column in ["year", "rating", "runtime", "votes_number"]:
+    column_data = movies_data[column]
+    print(f"Information on {column}")
+    print(f"Min: {column_data.min()}")
+    print(f"Max: {column_data.max()}")
+    print(f"Mean: {column_data.mean()}")
+    print(f"Median: {column_data.median()}")
+    print(f"Standard deviation: {column_data.std()}, \n")