37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
import pandas as pd
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from sklearn import preprocessing
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from sklearn.model_selection import train_test_split
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df = pd.read_csv('smart_grid_stability_augmented.csv')
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min_max_scaler = preprocessing.MinMaxScaler()
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df_norm_array = min_max_scaler.fit_transform(df.iloc[:, 0:-1])
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df_norm = pd.DataFrame(data=df_norm_array,
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columns=df.columns[:-1])
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df_norm['stabf'] = df['stabf']
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train, testAndValid = train_test_split(
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df_norm,
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test_size=0.2,
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random_state=42,
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stratify=df_norm['stabf'])
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test, valid = train_test_split(
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testAndValid,
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test_size=0.5,
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random_state=42,
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stratify=testAndValid['stabf'])
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def namestr(obj, namespace):
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return [name for name in namespace if namespace[name] is obj]
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dataset = df_norm
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for x in [dataset, train, test, valid]:
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print([q for q in namestr(x, globals()) if len(q) == max([len(w) for w in namestr(x, globals())])][-1])
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print("size:", len(x))
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print(x.describe(include='all'))
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print("class distribution", x.value_counts('stabf'))
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