pytorch data

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1cat_cols = ["Hour","AMorPM", "Weekday"]
2cont_cols = ['pickup_longitude',
3       'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude',
4       'passenger_count', 'dist_km']
5
6y_col = ["fare_amount"]
7
8for cat in cat_cols:
9    df[cat] = df[cat].astype("category")
10    
11df = shuffle(df, random_state=101)
12df.reset_index(drop=True, inplace=True)
13
14cats = np.stack([df[col].cat.codes.values for col in cat_cols], 1)
15conts = np.stack([df[col].values for col in cont_cols],1)
16cats = torch.tensor(cats, dtype=torch.int64)
17conts = torch.tensor(conts, dtype=torch.float)
18y = torch.tensor(df[y_col].values,dtype=torch.float).reshape(-1,1) #regression
19# classification: y = torch.tensor(df[y_col].values,dtype=torch.float).flatten()
20
21cat_sizes = [len(df[col].cat.categories) for col in cat_cols]
22emb_sizes = [(size, min(50, (size+1)//2)) for size in cat_sizes]
23
24b = 30000 # suggested batch size
25t = 5000  # suggested test size
26
27cat_train = cats[:b-t]
28cat_test  = cats[b-t:b]
29con_train = conts[:b-t]
30con_test  = conts[b-t:b]
31y_train   = y[:b-t]
32y_test    = y[b-t:b]

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